Biomarkers and Methods for Measuring and Monitoring Inflammatory Disease Activity

ABSTRACT

Biomarkers useful for diagnosing and assessing inflammatory disease are provided, along with kits for measuring their expression. The invention also provides predictive models, based on the biomarkers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples. The biomarkers include at least two biomarkers selected from the DAIMRK group and the score is a disease activity index (DAI).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to and claims the benefit of U.S.Provisional Application No. 61/252,110, filed on Oct. 15, 2009, U.S.Provisional Application No. 61/304,317 and U.S. Provisional Application61/355,087, filed on Jun. 15, 2010, all of which are herein incorporatedby reference in their entirety for all purposes.

INTRODUCTION

The present teachings are generally directed to biomarkers associatedwith inflammatory disease, and methods of characterizing biologicalconditions by scoring quantitative datasets derived from a subjectsample, as well as various other embodiments as described herein.

The section headings used herein are for convenience and organizationalpurposes only, and are not to be construed as limiting the subjectmatter described in any way. All literature and similar materials citedin this application, including but not limited to scientificpublications, articles, books, treatises, published patent applications,issued patents, and internet web pages, regardless the format of suchliterature and similar materials, are expressly incorporated byreference in their entirety for any purpose.

BACKGROUND

This application is directed to the fields of bioinformatics andinflammatory and autoimmune diseases, with rheumatoid arthritis (RA) asan example of these diseases. The present teachings relate to methodsand compositions for assessing, diagnosing, monitoring, and selectingtreatment for inflammatory disease and autoimmune disease: e.g., RA.

RA is an example of an inflammatory disease, and is a chronic, systemicautoimmune disorder. It is one of the most common systemic autoimmunediseases worldwide. The immune system of the RA subject targets his/herown joints as well as other organs including the lung, blood vessels andpericardium, leading to inflammation of the joints (arthritis),widespread endothelial inflammation, and even destruction of jointtissue. Erosions and joint space narrowing are largely irreversible andresult in cumulative disability.

The precise etiology of RA has not been established, but underlyingdisease pathogenesis is complex and includes inflammation and immunedysregulation. The precise mechanisms involved are different inindividual subjects, and can change in those subjects over time.Variables such as race, sex, genetics, hormones, and environmentalfactors can impact the development and severity of RA disease. Emergingdata are also beginning to reveal the characteristics of new RA subjectsubgroups and complex overlapping relationships with other autoimmunedisorders. Disease duration and level of inflammatory activity is alsoassociated with other comorbidities such as risk of lymphoma,extra-articular manifestations, and cardiovascular disease. See, e.g.,S. Banerjee et al., Am. J. Cardiol. 2008, 101(8):1201-1205; E. Baecklundet at, Arth. Rheum. 2006, 54(3):692-701; and, N. Goodson et al., Ann.Rheum. Dis. 2005, 64(11):1595-1601. Because of the complexity of RA, itis difficult to develop a single test that can accurately andconsistently assess, quantify, and monitor RA disease activity in everysubject.

Traditional models for treating RA are based on the expectation thatcontrolling disease activity (i.e., inflammation) in an RA subjectshould slow or prevent disease progression, in terms of tissuedestruction, cartilage loss and joint erosion. There is evidence,however, that disease activity and disease progression can be uncoupled,and may not always function completely in tandem. Indeed, different cellsignaling pathways and mediators are involved in these two processes.See W. van den Berg et al., Arth. Rheum. 2005, 52:995-999. Theuncoupling of disease progression and disease activity is described in anumber of RA clinical trials and animal studies. See, e.g., P E Lipskyet al., N. Engl. J. Med. 2003, 343:1594-602; A K Brown et al., Arth.Rheum. 2006, 54:3761-3773; and, A R Pettit et al., Am. J. Pathol. 2001,159:1689-99. Studies of RA subjects indicate limited association betweenclinical and radiographic responses. See E. Zatarain and V. Strand, Nat.Clin. Pract. Rheum. 2006, 2(11):611-618 (Review). RA subjects have beendescribed who demonstrated radiographic benefits from combinationtreatment with infliximab and methotrexate (MTX), yet did notdemonstrate any clinical improvement, as measured by DAS (DiseaseActivity Score) and CRP (C-reactive protein). See J S Smolen et al.,Arth. Rheum. 2005, 52(4):1020-30. To best study the uncoupling ofdisease progression and activity (erosion and inflammation,respectively), and to analyze the relationship between disease activityand progression, RA subjects should be assessed frequently for bothdisease activity and progression.

An increasing number of studies have demonstrated that frequentmonitoring of disease activity (known as “tight control”) results inquicker improvement in and better subject outcomes. The underlyingreason for regularly monitoring an RA subject's disease activity, usingappropriate and validated assessment tools, is because RA disease ingeneral displays a highly variable and unpredictable course ofprogression. In chronic inflammatory diseases, and RA in particular,treatment is ultimately aimed at remission. It has been shown that agreater proportion of subjects with monthly disease activity assessmentswere in remission at one year compared to those receiving standard ofcare (standard of care being no assessment of disease activity, orassessments made less frequently than monthly); and further, thatsubjects with monthly disease activity assessments had betterradiographic outcomes and physical function compared to those withstandard of care. See Y P M Goekoop-Ruiterman et al., Ann. Rheum. Dis.2009 (Epublication Jan. 20, 2009); C. Grigor et al., Lancet 2004,364:263-269; W. Kievit et al., Ann. Rheum. Dis. 2008, 67(9):1229-1234;T. Mottonen et al., Arth. Rheum. 2002, 46(4):894-898; V K Ranganath etal., J. Rhewn. 2008, 35:1966-1971; T. Sokka et al., Clin. Exp. Rheum.2006, 24(Suppl. 43):S74-76; L H D van Tuyl et al., Ann. Rheum. Dis.2008, 67:1574-1577; and, S M M Verstappen et al., Ann. Rheum. Dis. 2007,66:1443-1449. The ability to effectively monitor disease activity wouldallow for tight control of subjects, thus leading to better subjectoutcomes.

There is a need to classify subjects by disease activity in order toensure that each receives treatment that is appropriate and optimizedfor that patient. In treatment for RA, for example, the use ofdisease-modifying anti-rheumatic drug (DMARD) combinations has becomeaccepted for subjects who fail to respond to a single DMARD. Studiesanalyzing treatment with MTX alone and treatment with MTX in combinationwith other DMARDs demonstrate that in DMARD-naive subjects, the balanceof efficacy versus toxicity favors MTX monotherapy, while inDMARD-inadequate responders, the evidence is inconclusive. In regards tobiologics (e.g., anti-TNFα), studies support the use of biologics incombination with MTX in subjects with early RA, or in subjects withestablished RA who have not yet been treated with MTX. The number ofdrugs available for treating RA is increasing; from this it follows thatthe number of possible combinations of these drugs is increasing aswell. In addition, the chronological order in which each drug in acombination is administered can be varied depending on the needs of thesubject. For the clinician to apply a simple trial-and-error process tofind the optimum treatment for the RA subject from among the myriad ofpossible combinations, the clinician runs the risk of under- or overtreating the subject. Irreversible joint damage for the subject could bethe result. See, e.g., A K Brown et al., Arth. Rheum. 2008,58(10):2958-2967, and G. Cohen et al., Ann. Rheum. Dis. 2007,66:358-363. Clearly there exists a need to accurately classify subjectsby disease activity, in order to establish their optimal treatmentregimen.

Current clinical management and treatment goals, in the case of RA,focus on the suppression of disease activity with the goal of improvingthe subject's functional ability and slowing the progression of jointdamage. Clinical assessments of RA disease activity include measuringthe subject's difficulty in performing activities, morning stiffness,pain, inflammation, and number of tender and swollen joints, an overallassessment of the subject by the physician, an assessment by the subjectof how good s/he feels in general, and measuring the subject'serythrocyte sedimentation rate (ESR) and levels of acute phasereactants, such as CRP. Composite indices comprising multiple variables,such as those just described, have been developed as clinical assessmenttools to monitor disease activity. The most commonly used are: AmericanCollege of Rheumatology (ACR) criteria (D T Felson et al., Arth. Rheum.1993, 36(6):729-740 and D T Felson et al., Arth. Rheum. 1995,38(6):727-735); Clinical Disease Activity Index (CDAI) (D. Aletaha etal., Arth. Rheum. 2005, 52(9):2625-2636); the DAS (MLL Prevoo et al.,Arth. Rheum. 1995, 38(1):44-48 and A M van Gestel et al., Arth. Rheum.1998, 41(10):1845-1850); Rheumatoid Arthritis Disease Activity Index(RADAI) (G. Stucki et al., Arth. Rheum. 1995, 38(6):795-798); and,Simplified Disease Activity Index (SDAI) (J S Smolen et al.,Rheumatology (Oxford) 2003, 42:244-257).

Current laboratory tests routinely used to monitor disease activity inRA subjects, such as CRP and ESR, are relatively non-specific (e.g., arenot RA-specific and cannot be used to diagnose RA), and cannot be usedto determine response to treatment or predict future outcomes. See,e.g., L. Gossec et al., Ann. Rheum. Dis. 2004, 63(6):675-680; E J AKroot et al., Arth. Rheum. 2000, 43(8):1831-1835; H. Makinen et al.,Ann. Rheum. Dis. 2005, 64(10):1410-1413; Z. Nadareishvili et al., Arth.Rheum. 2008, 59(8): 1090-1096; N A Khan et al., Abstract, ACR/ARHPScientific Meeting 2008; T A Pearson et al., Circulation 2003,107(3):499-511; M J Plant et al., Arth. Rheum. 2000, 43(7):1473-1477; T.Pincus et al., Clin. Exp. Rheum. 2004, 22(Suppl. 35):S50-S56; and, P MRidker et al., NEJM 2000, 342(12):836-843. In the case of ESR and CRP,RA subjects may continue to have elevated ESR or CRP levels despitebeing in clinical remission (and non-RA subjects may display elevatedESR or CRP levels). Some subjects in clinical remission, as determinedby DAS, continue to demonstrate continued disease progressionradiographically, by erosion. Furthermore, some subjects who do notdemonstrate clinical benefits still demonstrate radiographic benefitsfrom treatment. See, e.g., F C Breedveld et al., Arth. Rheum. 2006,54(1):26-37. Clearly, in order to predict future outcome and treat theRA subject accordingly, there is a need for clinical assessment toolsthat accurately assess an RA subject's disease activity level and thatact as predictors of future course of disease.

Clinical assessments of disease activity contain subjective measurementsof RA, such as signs and symptoms, and subject-reported outcomes, alldifficult to quantify consistently. In clinical trials, the DAS isgenerally used for assessing RA disease activity. The DAS is an indexscore of disease activity based in part on these subjective parameters.Besides its subjectivity component, another drawback to use of the DASas a clinical assessment of RA disease activity is its invasiveness. Thephysical examination required to derive a subject's DAS can be painful,because it requires assessing the amount of tenderness and swelling inthe subject's joints, as measured by the level of discomfort felt by thesubject when pressure is applied to the joints. Assessing the factorsinvolved in DAS scoring is also time-consuming. Furthermore, toaccurately determine a subject's DAS requires a skilled assessor so asto minimize wide inter- and intra-operator variability. A method ofclinically assessing disease activity is needed that is less invasiveand time-consuming than DAS, and more consistent, objective andquantitative, while being specific to the disease assessed (such as RA).

Developing biomarker-based tests (e.g., measuring cytokines), e.g.specific to the clinical assessment of RA, has proved difficult inpractice because of the complexity of RA biology—the various molecularpathways involved and the intersection of autoimmune dysregulation andinflammatory response. Adding to the difficulty of developingRA-specific biomarker-based tests are the technical challenges involved;e.g., the need to block non-specific matrix binding in serum or plasmasamples, such as rheumatoid factor (RF) in the case of RA. The detectionof cytokines using bead-based immunoassays, for example, is not reliablebecause of interference by RF; hence, RF-positive subjects cannot betested for RA-related cytokines using this technology (and RF removalmethods attempted did not significantly improve results). See S.Churchman et al., Ann. Rheum. Dis. 2009, 68:A1-A56, Abstract A77.Approximately 70% of RA subjects are RF-positive, so any biomarker-basedtest that cannot assess RF-positive patients is obviously of limiteduse.

To achieve the maximum therapeutic benefits for individual subjects, itis important to be able to specifically quantify and assess thesubject's disease activity at any particular time, determine the effectsof treatment on disease activity, and predict future outcomes. Noexisting single biomarker or multi-biomarker test produces resultsdemonstrating a high association with level of RA disease activity. Theembodiments of the present teachings identify multiple serum biomarkersfor the accurate clinical assessment of disease activity in subjectswith chronic inflammatory disease, such as RA, along with methods oftheir use.

SUMMARY

The present teachings relate to biomarkers associated with inflammatorydisease, and with autoimmune disease, including RA, and methods of usingthe biomarkers to measure disease activity in a subject.

One embodiment provides a method for scoring a sample, said methodcomprising: receiving a first dataset associated with a first sampleobtained from a first subject, wherein said first dataset comprisesquantitative data for at least two markers selected from the groupconsisting of: apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3);calprotectin (heteropolymer of protein subunits S100A8 and S100A9);chemokine (C-C motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilageglycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP);epidermal growth factor (beta-urogastrone) (EGF); intercellular adhesionmolecule 1 (ICAM1); ICTP interleukin 18 (interferon-gamma-inducingfactor) (IL18); interleukin 1, beta (IL1B); interleukin 1 receptorantagonist (IL1RN); interleukin 6 (interferon, beta 2) (IL6);interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan sulfate;leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase)(MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3);pyridinoline (PYD); resistin (RETN); serum amyloid A1 (SAA1); tumornecrosis factor receptor superfamily, member 1A (TNFRSF1A); tumornecrosis factor (ligand) superfamily, member 13b (TNFSF13B, or BAFF);vascular cell adhesion molecule 1 (VCAM1); and, vascular endothelialgrowth factor A (VEGFA); and determining, a first DAI score from saidfirst dataset using an interpretation function, wherein said first DAIscore provides a quantitative measure of inflammatory disease activityin said first subject.

In one embodiment first dataset is obtained by a method comprisingobtaining said first sample from said first subject, wherein said firstsample comprises a plurality of analytes; contacting said first samplewith a reagent, generating a plurality of complexes between said reagentand said plurality of analytes; and detecting said plurality ofcomplexes to obtain said first dataset associated with said firstsample, wherein said first dataset comprises quantitative data for saidleast two markers.

In one embodiment said at least two markers are selected from the groupconsisting of: chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1);C-reactive protein, pentraxin-related (CRP); epidermal growth factor(beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6);leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase)(MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3);resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptorsuperfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1(VCAM1) and vascular endothelial growth factor A (VEGFA).

In one embodiment said at least two markers are selected from the groupconsisting of IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3,TNFRSF1A, RETN, and CHI3L1.

In one embodiment the method further comprises reporting said DAI scoreto said first subject.

In one embodiment said inflammatory disease activity is rheumatoidarthritis disease activity and further comprising predicting a Sharpscore change for said first subject, based on said DAI score.

In one embodiment said interpretation function is based on a predictivemodel.

In one embodiment said predictive model is developed using an algorithmcomprising a forward linear stepwise regression algorithm; a Lassoshrinkage and selection method for linear regression; or an Elastic Netfor regularization and variable selection for linear regression.

In one embodiment said algorithm is DAIscore=(0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPOA))+(0.36*ln(CRP/10⁶+1))+0.96;wherein IPTJC=Improved PTJC=max(0.1739*PTJC+0.7865*PSJC,0);IPSJC=Improved PSJC=max(0.1734*PTJC+0.7839*PSJC,0); PTJC=Prediction ofTender JointCount=−38.564+3.997*(SAA1)^(1/10)+17.331*(IL6)^(1/10)+4.665*(CHI3L1)^(1/10)−15.236*(EGF)^(1/10)+2.651*(TNFRSF1A)^(1/10)+2.641*(LEP)^(1/10)+4.026*(VEGFA)^(1/10)−1.47*(VCAM1)^(1/10);PSJC=Prediction of Swollen JointCount=−25.444+4.051*(SAA1)^(1/10)+16.154*(IL6)^(1/10)−11.847*(EGF)^(1/10)+3.091*(CHI3L1)^(1/10)+0.353*(TNFRSF1A)^(1/10);PPGA=Prediction of Patient GlobalAssessment=−13.489+5.474*(IL6)^(1/10)+0.486*(SAA1)^(1/10)+2.246*(MMP1)^(1/10)+1.684*(leptin)^(1/10)+4.14*(TNFRSF1A)^(1/10)+2.292*(VEGFA)^(1/10)−1.898*(EGF)^(1/10)+0.028*(MMP3)^(1/10)−2.892*(VCAM1)^(1/10)−0.506*(RETN)^(1/10)wherein units for all biomarkers are pg/mL.

In one embodiment said algorithm is DAIscore=(0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(CRP+1))+0.96;wherein IPTJC=Improved PTJC=max(0.1739*PTJC+0.7865*PSJC,0);IPSJC=Improved PSJC=max(0.1734*PTJC+0.7839*PSJC,0); PTJC=Prediction ofTender JointCount=−38.564+3.997*(SAA1)^(1/10)+17.331*(IL6)^(1/10)+4.665*(CHI3L1)^(1/10)−15.236*(EGF)^(1/10)+2.651*(TNFRSF1A)^(1/10)+2.641*(LEP)^(1/10)+4.026*(VEGFA)^(1/10)−1.47*(VCAM1)^(1/10);PSJC=Prediction of Swollen JointCount=−25.444+4.051*(SAA1)^(1/10)+16.154*(IL6)^(1/10)−11.847*(EGF)^(1/10)+3.091*(CHI3L)^(1/10)+0.353*(TNFRSF1A)^(1/10);PPGA=Prediction of Patient GlobalAssessment=−13.489+5.474*(IL6)^(1/10)+0.486*(SAA1)^(1/10)+2.246*(MMP1)^(1/10)+1.684*(leptin)^(1/10)+4.14*(TNFRSF1A)^(1/10)+2.292*(VEGFA)^(1/10)−1.898*(EGF)^(1/10)+0.028*(MMP3)^(1/10)−2.892*(VCAM1)^(1/10)−0.506*(RETN)^(1/10)wherein units for CRP are mg/L and for other biomarkers are pg/mL.

In one embodiment, the method further comprises determining a scaled DAIscore wherein said scaled DAI score=round(max(min((DAI score)*10.53+1,100),1)).

In one embodiment said first DAI score is predictive of a clinicalassessment.

In one embodiment said clinical assessment is selected from the groupconsisting of: a DAS, a DAS28, a Sharp score, a tender joint count(TJC), and a swollen joint count (SJC).

In one embodiment said clinical assessment is a DAS.

In one embodiment said clinical assessment is a DAS28.

In one embodiment said DAS28 comprises a component selected from thegroup consisting of tender joint count (TJC), the swollen joint count(SJC), and the patient global health assessment.

In one embodiment said clinical assessment is TJC and said first datasetcomprises quantitative data for at least one marker selected from thegroup consisting of CHI3L1, EGF, IL6, LEP, SAA1, TNFRSF1A, VCAM1, andVEGFA.

In one embodiment said clinical assessment is SJC and said first datasetcomprises quantitative data for at least one marker selected from thegroup consisting of CHI3L1, EGF, IL6, SAA1, and TNFRSF1A.

In one embodiment said clinical assessment is patient global healthassessment and said first dataset comprises quantitative data for atleast one marker selected from the group consisting of EGF, IL6, LEP,MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA.

In one embodiment, the method further comprises receiving a seconddataset associated with a second sample obtained from said firstsubject, wherein said first sample and said second sample are obtainedfrom said first subject at different times; determining a second DAIscore from said second dataset using said interpretation function; andcomparing said first DAI score and said second DAI score to determine achange in said DAI scores, wherein said change indicates a change insaid inflammatory disease activity in said first subject.

In one embodiment said inflammatory disease activity is rheumatoidarthritis activity and said indicated change in rheumatoid arthritisdisease activity indicates the presence, absence or extent of thesubject's response to a therapeutic regimen.

In one embodiment, the method further comprises determining a rate ofsaid change in DAI scores, wherein said rate indicates the extent ofsaid first subject's response to a therapeutic regimen.

In one embodiment said inflammatory disease activity is rheumatoidarthritis disease activity and further comprising predicting a Sharpscore change rate for said first subject, based on said indicated changein rheumatoid arthritis disease activity.

In one embodiment the method further comprises determining a prognosisfor rheumatoid arthritis progression in said first subject based on saidpredicted Sharp score change rate.

In one embodiment said inflammatory disease is rheumatoid arthritis.

In one embodiment said inflammatory disease is undifferentiatedarthritis.

In one embodiment one of said at least two markers is CRP or SAA1.

In one embodiment said DAI score is used as an inflammatory diseasesurrogate endpoint, the inflammatory disease may be rheumatoidarthritis.

In one embodiment a method for determining a presence or absence ofrheumatoid arthritis in a subject is provided, the method comprisingdetermining DAI scores according the disclosed methods for subjects in apopulation wherein said subjects are negative for rheumatoid arthritis;deriving an aggregate DAI value for said population based on saiddetermined DAI scores; determining a second DAI score for a secondsubject; comparing the aggregate DAI value to the second DAI score; anddetermining a presence or absence of rheumatoid arthritis in said secondsubject based on said comparison.

In one embodiment said first subject has received a treatment forrheumatoid arthritis, and the method further comprises the steps ofdetermining a second DAI score according to the disclosed method for asecond subject wherein said second subject is of the same species assaid first subject and wherein said second subject has receivedtreatment for rheumatoid arthritis; comparing said first DAI score tosaid second DAI score; and determining a treatment efficacy for saidfirst subject based on said score comparison.

In one embodiment the method further comprises determining a response torheumatoid arthritis therapy based on said DAI score.

In one embodiment the method further comprises selecting a rheumatoidarthritis therapeutic regimen based on said DAI score.

In one embodiment the method further comprises determining a rheumatoidarthritis treatment course based on said DAI score.

In one embodiment the method further comprises rating a rheumatoidarthritis disease activity as low or high based on said DAI score.

In one embodiment said predictive model performance is characterized byan AUC ranging from 0.60 to 0.99.

In one embodiment said predictive model performance is characterized byan AUC ranging from 0.70 to 0.79.

In one embodiment said predictive model performance is characterized byan AUC ranging from 0.80 to 0.89.

In one embodiment said at least two markers comprise (APOA1 and IL8),(Calprotectin and CRP), (Calprotectin and EGF), (Calprotectin and IL8),(CRP and APOA1), (CRP and APOC3), (CRP and CCL22), (CRP and CHI3L1),(CRP and EGF), (CRP and ICAM1), (CRP and IL1B), (CRP and IL6), (CRP andIL6R), (CRP and IL8), (CRP and LEP), (CRP and MMP1), (CRP and MMP3),(CRP and RETN), (CRP and SAA1), (CRP and TNFRSF1A), (CRP and VCAM1),(CRP and VEGF), (EGF and APOA1), (EGF and CHI3L1), (EGF and ICAM1), (EGFand IL8), (EGF and LEP), (EGF and MMP1), (EGF and TNFRSF1A), (EGF andVCAM1), (ICAM1 and IL8), (IL1RN and CRP), (IL1RN and EGF), (IL1RN andIL8), (IL8 and APOC3), (IL8 and CCL22), (IL8 and CHI3L), (IL8 and IL6),(IL8 and IL6R), (IL8 and TNFRSF1A), (LEP and IL8), (MMP3 and IL8), (RETNand IL8), (SAA1 and EGF), (SAA1 and IL8), (SAA1 and LEP), (SAA1 andRETN), or (VCAM1 and IL8).

In one embodiment said at least two markers comprise (calprotectin andCHI3L1), (calprotectin and interleukin), (calprotectin and LEP),(calprotectin and pyridinoline), (calprotectin and RETN), (CCL22 andcalpotectin), (CCL22 and CRP), (CCL22 and IL6), (CCL22 and SAA1), (CRPand calprotectin), (CRP and CHI3L1), (CRP and EGF), (CRP and ICAM1),(CRP and IL1B), (CRP and IL1RN), (CRP and IL6), (CRP and IL6R), (CRP andIL8), (CRP and LEP), (CRP and MMP1), (CRP and MMP3), (CRP andpyridinoline), (CRP and RETN), (CRP and SAA1), (CRP and TNFRSF1A), (CRPand VCAM1), (CRP and VEGFA), (EGF and calprotectin), (EGF and IL6), (EGFand SAA1), (ICAM1 and calprotectin), (ICAM1 and IL6), (ICAM1 and SAA1),(IL1B and calprotectin), (IL1B and IL6), (IL1B and MMP3), (IL1B andSAA1), (IL6 and calprotectin), (IL6 and CHI3L1), (IL6 and IL1RN), (IL6and IL8), (IL6 and LEP), (IL6 and MMP1), (IL6 and MMP3), (IL6 andpyridinoline), (IL6 and RETN), (IL6 and SAA1), (IL6 and TNFRSF1A), (IL6and VCAM1), (IL6 and VEGFA), (IL6R and calprotectin), (IL6R and IL6),(IL6R and SAA1), (IL8 and calprotectin), (IL8 and MMP3), (IL8 and SAA1),(MMP1 and calprotectin), (MMP1 and SAA1), (MMP3 and calprotectin), (MMP3and CHI3L1), (MMP3 and SAA1), (SAA1 and calprotectin), (SAA1 andCHI3L1), (SAA1 and IL1RN), (SAA1 and LEP), (SAA1 and pyridinoline),(SAA1 and RETN), (SAA1 and TNFRSF1A), (SAA1 and VCAM1), (SAA1 andVEGFA), (TNFRSF1A and calprotectin), (VCAM1 and calprotectin); or,(VEGFA and calprotectin)

In one embodiment said at least two markers comprise one set of markersselected from the group consisting of TWOMRK Set Nos. 1 through 208 ofFIG. 1.

In one embodiment said at least two markers comprise one set of markersselected from the group consisting of TWOMRK Set Nos. 1 through 157 ofFIG. 17.

In one embodiment said at least two markers comprises at least threemarkers selected from the group consisting of: apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); chemokine (C-C motif) ligand 22(CCL22); chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); ICTP;C-reactive protein, pentraxin-related (CRP); epidermal growth factor(beta-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1);interleukin 18 (interferon-gamma-inducing factor) (IL18); interleukin 1,beta (IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R); interleukin 8(IL8); keratan sulfate; leptin (LEP); matrix metallopeptidase 1(interstitial collagenase) (MMP1); matrix metallopeptidase 3(stromelysin 1, progelatinase) (MMP3); resistin (RETN); calprotectin(heteropolymer of protein subunits S100A8 and S100A9); serum amyloid A1(SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); vascularendothelial growth factor A (VEGFA); and, pyridinoline (PYD).

In one embodiment said at least two markers comprises one set of threemarkers selected from the group consisting of THREEMRK Set Nos. 1through 378 of FIG. 2 and THREEMRK Set Nos. 1 through 236 of FIG. 18.

In one embodiment said at least two markers comprises one set of threemarkers selected from the group consisting of THREEMRK Set Nos. 1through 236 of FIG. 18.

In one embodiment said at least two markers comprises at least fourmarkers selected from the group consisting of: apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); chemokine (C-C motif) ligand 22(CCL22); chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L); ICTP;C-reactive protein, pentraxin-related (CRP); epidermal growth factor(beta-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1);interleukin 18 (interferon-gamma-inducing factor) (IL18); interleukin 1,beta (IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R); interleukin 8(IL8); keratan sulfate; leptin (LEP); matrix metallopeptidase 1(interstitial collagenase) (MMP1); matrix metallopeptidase 3(stromelysin 1, progelatinase) (MMP3); resistin (RETN); calprotectin(heteropolymer of protein subunits S100AS and S100A9); serum amyloid A1(SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); vascularendothelial growth factor A (VEGFA); and, pyridinoline (PYD).

In one embodiment said at least two markers comprises one set of fourmarkers selected from the group consisting of FOURMRK Set Nos. 1 through54 of FIG. 3.

In one embodiment said at least two markers comprises one set of fourmarkers selected from the group consisting of FOURMRK Set Nos. 1 through266 of FIG. 19.

In one embodiment said at least two markers comprises at least fivemarkers selected from the group consisting of: apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); chemokine (C-C motif) ligand 22(CCL22); chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); ICTP;C-reactive protein, pentraxin-related (CRP); epidermal growth factor(beta-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1);interleukin 18 (interferon-gamma-inducing factor) (IL18); interleukin 1,beta (IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R); interleukin 8(IL8); keratan sulfate; leptin (LEP); matrix metallopeptidase 1(interstitial collagenase) (MMP1); matrix metallopeptidase 3(stromelysin 1, progelatinase) (MMP3); resistin (RETN); calprotectin(heteropolymer of protein subunits S100A8 and S100A9); serum amyloid A1(SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); vascularendothelial growth factor A (VEGFA); and, pyridinoline (PYD).

In one embodiment said at least two markers comprises one set of fivemarkers selected from the group consisting of FIVEMRK Set Nos. 1 through44 of FIG. 4.

In one embodiment said at least two markers comprises one set of fivemarkers selected from the group consisting of FIVEMRK Set Nos. 1 through236 of FIG. 20.

In one embodiment said at least two markers comprises at least sixmarkers selected from the group consisting of: apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); chemokine (C-C motif) ligand 22(CCL22); chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); ICTP,C-reactive protein, pentraxin-related (CRP); epidermal growth factor(beta-urogastrone) (EGF); intercellular adhesion molecule 1 (ICAM1);interleukin 18 (interferon-gamma-inducing factor) (IL18); interleukin 1,beta (IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R); interleukin 8(IL8); keratan sulfate; leptin (LEP); matrix metallopeptidase 1(interstitial collagenase) (MMP1); matrix metallopeptidase 3(stromelysin 1, progelatinase) (MMP3); resistin (RETN); calprotectin(heteropolymer of protein subunits S100A8 and S100A9); serum amyloid A1(SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); vascularendothelial growth factor A (VEGFA); and, pyridinoline (PYD).

In one embodiment said at least two markers comprises one set of sixmarkers selected from the group consisting of SIXMRK Set Nos. 1 through84 of FIG. 5.

In one embodiment said at least two markers comprises one set of sixmarkers selected from the group consisting of SIXMRK Set Nos. 1 through192 of FIG. 21.

In one embodiment said at least two markers comprises calprotectin.CCL22, CRP, EGF, ICAM1, CHI3L1, ICTP, IL1B, IL1RA, IL6, IL6R, IL8, LEP,MMP1, MMP3, pyridinoline, RETN, SAA1, TNFRSF1A, VCAM1 and VEGFA.

In one embodiment said at least two markers comprises IL6, EGF, VEGFA,LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.

Also provided are computer-implemented methods, systems andcomputer-readable storage mediums with program code for carrying out thedisclosed methods.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. The drawings are not intended tolimit the scope of the present teachings in any way.

FIG. 1 depicts a list of two-biomarker (TWOMRK) sets or panels, asdescribed in certain embodiments of the present teachings, and accordingto Example 1. Models were run for all possible two-biomarkercombinations of the DAIMRK biomarkers analyzed in Example 1. DAI scoresderived from the levels of a set of biomarkers comprising the TWOMRKsets of biomarkers in FIG. 1 demonstrated a strong predictive ability toclassify subject disease activity, as evidenced by the AUC values shown(greater than or equal to 0.60). In this and following figures,correlations of the DAI scores with DAS28 are shown by r, as estimatedusing 100 test set cross-validation.

FIG. 2 depicts a list of three-biomarker (THREEMRK) sets or panels, asdescribed in certain embodiments of the present teachings, and accordingto the methods of Example 1. DAI scores derived from the levels of a setof biomarkers comprising the THREEMRK sets of biomarkers in FIG. 2demonstrated a strong association with DAS28-CRP, as evidenced by theAUC values shown (greater than or equal to 0.65). Note that the list ofTHREEMRK sets in FIG. 2 does not contain any panels comprising the twobiomarkers of FIG. 1, as this would be redundant (FIG. 1 describesbiomarker sets comprising the TWOMRK sets, not consisting of the TWOMRKsets).

FIG. 3 depicts a list of four-biomarker (FOURMRK) sets or panels, asdescribed in certain embodiments of the present teachings, and accordingto Example 1. DAI scores derived from the levels of a set of biomarkerscomprising the FOURMRK sets of biomarkers in FIG. 3 demonstrated astrong association with DAS28-CRP, as evidenced by the AUC values shown(greater than or equal to 0.70). Note that the list of FOURMRK sets inFIG. 3 does not contain any panels comprising the three biomarkers ofFIG. 2, as this would be redundant (FIG. 2 describes biomarker setscomprising the THREEMRK sets, not consisting of the THREEMRK sets).

FIG. 4 depicts a list of five-biomarker (FIVEMRK) sets or panels, asdescribed in certain embodiments of the present teachings, and accordingto Example 1. DAI scores derived from the levels of a set of biomarkerscomprising the FIVEMRK sets of biomarkers in FIG. 4 demonstrated astrong association with DAS28-CRP, as evidenced by the AUC values shown(greater than or equal to 0.70). Note that the list of FIVEMRK sets inFIG. 4 does not contain any panels comprising the four biomarkers ofFIG. 3, as this would be redundant (FIG. 3 describes biomarker setscomprising the FOURMRK sets, not consisting of the FOURMRK sets).

FIG. 5 depicts a list of six-biomarker (SIXMRK) sets or panels, asdescribed in certain embodiments of the present teachings, and accordingto Example 1. DAI scores derived from the levels of a set of biomarkerscomprising the SIXMRK sets of biomarkers in FIG. 5 demonstrated a strongassociation with DAS28-CRP, as evidenced by the AUC values shown(greater than or equal to 0.70). Note that the list of SIXMRK sets inFIG. 5 does not contain any panels comprising the five biomarkers ofFIG. 4, as this would be redundant (FIG. 4 describes biomarker setscomprising the FIVEMRK sets, not consisting of the FIVEMRK sets).

FIG. 6 is a flow diagram, which describes an example of a method fordeveloping a model that can be used to determine the inflammatorydisease activity of a person or population.

FIG. 7 is a flow diagram, which describes an example of a method forusing the model of FIG. 6 to determine the inflammatory disease activityof a subject or population.

FIG. 8 depicts the cumulative distribution function for p-values andFalse Discovery Rate, “FDR,” as related to the output of the DAS28 andother response variables of Example 1, where the FDR was used asmultiple testing correction, according to the following: let k be thelargest i for which p_(i)≦i/m*α; reject all H_(i), i=1, . . . , m. Inthis equation the variable a is a pre-specified probability of afalse-positive (Type I) error, typically 0.05, and H is a hypothesis.

FIG. 9 depicts a correlation matrix between the continuous clinicalvariables and biomarkers of Example 1. Darker gray indicates positivecorrelation, and lighter gray indicates negative correlation.

FIG. 11 depicts the three-dimensional PCA plot of Example 1. Each pointrepresents a subject.

FIG. 12 depicts the use of ROC and AUC to show the ability of DAI scoresto classify subjects into high/low disease groups (dichotomized on a DASof 2.67, where DAS<2.67 is remission) across all DAI cut-off points in100 cross-validations. The curve represents the average ROC curvesacross 100 cross-validations. See Example 1.

FIG. 13 depicts the use of ROC and AUC to show the ability of the DAIscore to classify subjects into high/low disease groups (dichotomized ona DAS of 3.9, the median of the DAS values in the data) across all DAIcut-off points in 100 cross-validations. The curve represents theaverage ROC curves across 100 cross-validations.

FIG. 14 depicts the accuracy (ACC) and error rates (ERR) of the 100cross-validation iterations of Example 2, where a DAS28-CRP cut-off of2.67 was used. Shown are the results of applying the Lasso and ElasticNet models.

FIG. 15 depicts the accuracy and error rates of the 100 cross-validationiterations of Example 2, where a DAS28-CRP cut-off of 3.94 was used.Shown are the results of applying the Lasso and Elastic Net models.

FIG. 16 is a high-level block diagram of a computer (1600). Illustratedare at least one processor (1602) coupled to a chipset (1604). Alsocoupled to the chipset (1604) are a memory (1606), a storage device(1608), a keyboard (1610), a graphics adapter (1612), a pointing device(1614), and a network adapter (1616). A display (1618) is coupled to thegraphics adapter (1612). In one embodiment, the functionality of thechipset (1604) is provided by a memory controller hub 1620) and an I/Ocontroller hub (1622). In another embodiment, the memory (1606) iscoupled directly to the processor (1602) instead of the chipset (1604).The storage device 1608 is any device capable of holding data, like ahard drive, compact disk read-only memory (CD-ROM), DVD, or asolid-state memory device. The memory (1606) holds instructions and dataused by the processor (1602). The pointing device (1614) may be a mouse,track ball, or other type of pointing device, and is used in combinationwith the keyboard (1610) to input data into the computer system (1600).The graphics adapter (1612) displays images and other information on thedisplay (1618). The network adapter (1616) couples the computer system(1600) to a local or wide area network.

FIG. 17 depicts another list of two-biomarker (TWOMRK) sets or panels,as described in certain embodiments of the present teachings, andaccording to Example 7. Models were run for all possible two-biomarkercombinations of the DAIMRK biomarkers analyzed in Example 7. DAI scoresderived from the levels of a set of biomarkers comprising the TWOMRKsets of biomarkers in FIG. 17 demonstrated a strong predictive abilityto classify subject disease activity, as evidenced by the AUC valuesshown (greater than or equal to 0.60).

FIG. 18 depicts another list of three-biomarker (THREEMRK) sets orpanels, as described in certain embodiments of the present teachings,and according to the methods of Example 7. DAI scores derived from thelevels of a set of biomarkers comprising the THREEMRK sets of biomarkersin FIG. 18 demonstrated a strong association with DAS28-CRP, asevidenced by the AUC values shown (greater than or equal to 0.60). Notethat the list of THREEMRK sets in FIG. 2 does not contain any panelscomprising the two biomarkers of FIG. 17, as this would be redundant(FIG. 17 describes biomarker sets comprising the TWOMRK sets, notconsisting of the TWOMRK sets).

FIG. 19 depicts another list of four-biomarker (FOURMRK) sets or panels,as described in certain embodiments of the present teachings, andaccording to Example 7. DAI scores derived from the levels of a set ofbiomarkers comprising the FOURMRK sets of biomarkers in FIG. 19demonstrated a strong association with DAS28-CRP, as evidenced by theAUC values shown (greater than or equal to 0.65). Note that the list ofFOURMRK sets in FIG. 19 does not contain any panels comprising the threebiomarkers of FIG. 18, as this would be redundant (FIG. 18 describesbiomarker sets comprising the THREEMRK sets, not consisting of theTHREEMRK sets).

FIG. 20 depicts another list of five-biomarker (FIVEMRK) sets or panels,as described in certain embodiments of the present teachings, andaccording to Example 7. DAI scores derived from the levels of a set ofbiomarkers comprising the FIVEMRK sets of biomarkers in FIG. 20demonstrated a strong association with DAS28-CRP, as evidenced by theAUC values shown (greater than 0.65). Note that the list of FIVEMRK setsin FIG. 20 does not contain any panels comprising the four biomarkers ofFIG. 19, as this would be redundant (FIG. 19 describes biomarker setscomprising the FOURMRK sets, not consisting of the FOURMRK sets).

FIG. 21 depicts another list of six-biomarker (SIXMRK) sets or panels,as described in certain embodiments of the present teachings, andaccording to Example 7. DAI scores derived from the levels of a set ofbiomarkers comprising the SIXMRK sets of biomarkers in FIG. 21demonstrated a strong association with DAS28-CRP, as evidenced by theAUC values shown (greater than 0.65). Note that the list of SIXMRK setsin FIG. 21 does not contain any panels comprising the five biomarkers ofFIG. 20, as this would be redundant (FIG. 20 describes biomarker setscomprising the FIVEMRK sets, not consisting of the FIVEMRK sets).

FIG. 22 depicts a Venn diagram indicating biomarkers that were used topredict various DAS components in deriving a DAI score, as described inExample 11.

FIG. 23 depicts correlations of the DAI algorithm predictions and CRPwith clinical assessments of disease activity, as described in Example11.

FIG. 24 depicts the DAI scores for subjects at baseline and six-monthvisits, according to the description in Example 11. DAI scores are shownby treatment arm and time point. Only subjects with DAI scores availableat both baseline and six months are shown.

DESCRIPTION OF VARIOUS EMBODIMENTS

These and other features of the present teachings will become moreapparent from the description herein. While the present teachings aredescribed in conjunction with various embodiments, it is not intendedthat the present teachings be limited to such embodiments. On thecontrary, the present teachings encompass various alternatives,modifications, and equivalents, as will be appreciated by those of skillin the art.

The present teachings relate generally to the identification ofbiomarkers associated with subjects having inflammatory and/orautoimmune diseases, such as for example RA, and that are useful indetermining or assessing disease activity.

Most of the words used in this specification have the meaning that wouldbe attributed to those words by one skilled in the art. Wordsspecifically defined in the specification have the meaning provided inthe context of the present teachings as a whole, and as are typicallyunderstood by those skilled in the art. In the event that a conflictarises between an art-understood definition of a word or phrase and adefinition of the word or phrase as specifically taught in thisspecification, the specification shall control. It must be noted that,as used in the specification and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the context clearlydictates otherwise.

DEFINITIONS

“Accuracy” refers to the degree that a measured or calculated valueconforms to its actual value. “Accuracy” in clinical testing relates tothe proportion of actual outcomes (true positives or true negatives,wherein a subject is correctly classified as having disease or ashealthy/normal, respectively) versus incorrectly classified outcomes(false positives or false negatives, wherein a subject is incorrectlyclassified as having disease or as healthy/normal, respectively). Otherand/or equivalent terms for “accuracy” can include, for example,“sensitivity,” “specificity,” “positive predictive value (PPV),” “theAUC,” “negative predictive value (NPV),” “likelihood,” and “odds ratio.”“Analytical accuracy,” in the context of the present teachings, refersto the repeatability and predictability of the measurement process.Analytical accuracy can be summarized in such measurements as, e.g.,coefficients of variation (CV), and tests of concordance and calibrationof the same samples or controls at different times or with differentassessors, users, equipment, and/or reagents. See, e.g., R. Vasan,Circulation 2006, 113(19):2335-2362 for a summary of considerations inevaluating new biomarkers.

The term “algorithm” encompasses any formula, model, mathematicalequation, algorithmic, analytical or programmed process, or statisticaltechnique or classification analysis that takes one or more inputs orparameters, whether continuous or categorical, and calculates an outputvalue, index, index value or score. Examples of algorithms include butare not limited to ratios, sums, regression operators such as exponentsor coefficients, biomarker value transformations and normalizations(including, without limitation, normalization schemes that are based onclinical parameters such as age, gender, ethnicity, etc.), rules andguidelines, statistical classification models, and neural networkstrained on populations. Also of use in the context of biomarkers arelinear and non-linear equations and statistical classification analysesto determine the relationship between (a) levels of biomarkers detectedin a subject sample and (b) the level of the respective subject'sdisease activity.

“ALLMRK” in the present teachings refers to a specific group, panel orset of biomarkers, as the term “biomarkers” is defined herein. Where thebiomarkers of certain embodiments of the present teachings are proteins,the gene symbols and names used herein are to be understood to refer tothe protein products of these genes, and the protein products of thesegenes are intended to include any protein isoforms of these genes,whether or not such isoform sequences are specifically described herein.Where the biomarkers are nucleic acids, the gene symbols and names usedherein are to refer to the nucleic acids (DNA or RNA) of these genes,and the nucleic acids of these genes are intended to include anytranscript variants of these genes, whether or not such transcriptvariants are specifically described herein. The ALLMRK group of thepresent teachings is the group of markers consisting of the following,where the name(s) or symbols in parentheses at the end of the markername generally refers to the gene name, if known, or an alias:adiponectin, C1Q and collagen domain containing (ADIPOQ); adrenomedullin(ADM); alkaline phosphatase, liver/bone/kidney (ALPL); amyloid Pcomponent, serum (APCS); advanced glycosylation end product-specificreceptor (AGER); apolipoprotein A-I (APOA1); apolipoprotein A-II(APOA2); apolipoprotein B (including Ag(x) antigen) (APOB);apolipoprotein C-II (APOC2); apolipoprotein C-III (APOC3);apolipoprotein E (APOE); bone gamma-carboxyglutamate (gla) protein(BGLAP, or osteocalcin); bone morphogenetic protein 6 (BMP6);calcitonin-related polypeptide beta (CALCB); calprotectin (dimer ofS100A8 and S100A9 protein subunits); chemokine (C-C motif) ligand 22(CCL22); CD40 ligand (CD40LG); chitinase 3-like 1 (cartilageglycoprotein-39) (CHI3L1, or YKL-40); cartilage oligomeric matrixprotein (COMP); C-reactive protein, pentraxin-related (CRP); CS3B3epitope, a cartilage fragment; colony stimulating factor 1 (macrophage)(CSF1, or MCSF); colony stimulating factor 2 (granulocyte-macrophage)(CSF2); colony stimulating factor 3 (granulocyte) (CSF3); cystatin C(CST3); epidermal growth factor (beta-urogastrone) (EGF); epidermalgrowth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogenehomolog, avian) (EGFR); erythropoietin (EPO); Fas (TNF receptorsuperfamily, member 6) (FAS); fibrinogen alpha chain (FGA); fibroblastgrowth factor 2 (basic) (FGF2); fibrinogen; fms-related tyrosine kinase1 (vascular endothelial growth factor/vascular permeability factorreceptor) (FLT1); fms-related tyrosine kinase 3 ligand (FLT3LG);fms-related tyrosine kinase 4 (FLT4); follicle stimulating hormone;follicle stimulating hormone, beta polypeptide (FSHB); gastricinhibitory polypeptide (GIP); ghrelin; ghrelin/obestatin prepropeptide(GHRL); growth hormone 1 (GH1); GLP1; hepatocyte growth factor (HGF);haptoglobin (HP); intercellular adhesion molecule 1 (ICAM1);intercellular adhesion molecule 3 (ICAM3); ICTP; interferon, alpha 1(IFNA1); interferon, alpha 2 (IFNA2); glial cell derived neurotrophicfactor (GDNF); interferon, gamma (IFNG); insulin-like growth factorbinding protein 1 (IGFBP1); interleukin 10 (IL10); interleukin 12;interleukin 12A (natural killer cell stimulatory factor 1, cytotoxiclymphocyte maturation factor 1, p35) (IL12A); interleukin 12B (naturalkiller cell stimulatory factor 2, cytotoxic lymphocyte maturation factor2, p40) (IL12B); interleukin 13 (IL13); interleukin 15 (IL15);interleukin 17A (IL17A); interleukin 18 (interferon-gamma-inducingfactor) (L8); interleukin 1, alpha (IL1A); interleukin 1, beta (IL1B);interleukin 1 receptor, type I (IL1R1); interleukin 1 receptor, type II(IL1R2); interleukin 1 receptor antagonist (IL1RN, or IL1RA);interleukin 2 (IL2); interleukin 2 receptor, interleukin 2 receptor,alpha (IL2RA); intedrleukin 3 (colony-stimulating factor, multiple)(IL3); interleukin 4 (IL4); interleukin 4 receptor (IL4R); interleukin 5(colony-stimulating factor, eosinophil) (IL5); interleukin 6(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R); interleukin 6signal transducer (gp130, oncostatin M receptor) (IL6ST); interleukin 7(IL7); interleukin 8 (IL8); insulin (INS); interleukin 9 (IL9); kinaseinsert domain receptor (a type III receptor tyrosine kinase) (KDR);v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT);keratan sulfate, or KS; leptin (LEP); leukemia inhibitory factor(cholinergic differentiation factor) (LIF); lymphotoxin alpha (TNFsuperfamily, member 1) (LTA); lysozyme (renal amyloidosis) (LYZ); matrixmetallopeptidase 1 (interstitial collagenase) (MMP1); matrixmetallopeptidase 10 (stromelysin 2) (MMP10); matrix metallopeptidase 2(gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase) (MMP2);matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); matrixmetallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IVcollagenase) (MMP9); myeloperoxidase (MPO); nerve growth factor (betapolypeptide) (NOF); natriuretic peptide precursor B (NPPB, orNT-proBNP); neurotrophin 4 (NTF4); platelet-derived growth factor alphapolypeptide (PDGFA); the dimer of two PDGFA subunits (or PDGF-AA); thedimer of one PDGFA subunit and one PDGFB subunit (or PDGF-AB);platelet-derived growth factor beta polypeptide (PDGFB); prostaglandinE2 (PGE2); phosphatidylinositol glycan anchor biosynthesis, class F(PIGF); proopiomelanocortin (POMC); pancreatic polypeptide (PPY);prolactin (PRL); pentraxin-related gene, rapidly induced by IL1 beta(PTX3, or pentraxin 3); pyridinoline (PYD); peptide YY (PYY); resistin(RETN); serum amyloid A1 (SAA1); selectin E (SELE); selectin L (SELL);selectin P (granule membrane protein 140 kDa, antigen CD62) (SELP);serpin peptidase inhibitor, clade E (nexin, plasminogen activatorinhibitor type 1), member 1 (SERPINE1); secretory leukocyte peptidaseinhibitor (SLPI); sclerostin (SOST); secreted protein, acidic,cysteine-rich (SPARC, or osteonectin); secreted phosphoprotein 1 (SPP1,or osteopontin); transforming growth factor, alpha (TGFA);thrombomodulin (THBD); tumor necrosis factor (TNF superfamily, member 2;or TNF-alpha) (TNF); tumor necrosis factor receptor superfamily, member11b (TNFRSF11B, or osteoprotegerin); tumor necrosis factor receptorsuperfamily, member 1A (TNFRSF1A); tumor necrosis factor receptorsuperfamily, member 1B (TNFRSF1B); tumor necrosis factor receptorsuperfamily, member 8 (TNFRSF8); tumor necrosis factor receptorsuperfamily, member 9 (TNFRSF9); tumor necrosis factor (ligand)superfamily, member 11 (TNFSF11, or RANKL); tumor necrosis factor(ligand) superfamily, member 12 (TNFSF12, or TWEAK); tumor necrosisfactor (ligand) superfamily, member 13 (TNFSF13, or APRIL); tumornecrosis factor (ligand) superfamily, member 13b (TNFSF13B, or BAFF);tumor necrosis factor (ligand) superfamily, member 14 (TNFSF14, orLIGHT); tumor necrosis factor (ligand) superfamily, member 18 (TNFSFS1);thyroid peroxidase (TPO); vascular cell adhesion molecule 1 (VCAM1);and, vascular endothelial growth factor A (VEGFA).

The term “analyte” in the context of the present teachings can mean anysubstance to be measured, and can encompass biomarkers, markers, nucleicacids, electrolytes, metabolites, proteins, sugars, carbohydrates, fats,lipids, cytokines, chemokines, growth factors, proteins, peptides,nucleic acids, oligonucleotides, metabolites, mutations, variants,polymorphisms, modifications, fragments, subunits, degradation productsand other elements. For simplicity, standard gene symbols may be usedthroughout to refer not only to genes but also gene products/proteins,rather than using the standard protein symbol; e.g., APOA1 as usedherein can refer to the gene APOA and also the protein ApoAI. Ingeneral, hyphens are dropped from analyte names and symbols herein(IL-6=IL6).

To “analyze” includes determining a value or set of values associatedwith a sample by measurement of analyte levels in the sample. “Analyze”may further comprise and comparing the levels against constituent levelsin a sample or set of samples from the same subject or other subject(s).The biomarkers of the present teachings can be analyzed by any ofvarious conventional methods known in the art. Some such methods includebut are not limited to: measuring serum protein or sugar or metaboliteor other analyte level, measuring enzymatic activity, and measuring geneexpression.

The term “antibody” refers to any immunoglobulin-like molecule thatreversibly binds to another with the required selectivity. Thus, theterm includes any such molecule that is capable of selectively bindingto a biomarker of the present teachings. The term includes animmunoglobulin molecule capable of binding an epitope present on anantigen. The term is intended to encompass not only intactimmunoglobulin molecules, such as monoclonal and polyclonal antibodies,but also antibody isotypes, recombinant antibodies, bi-specificantibodies, humanized antibodies, chimeric antibodies, anti-idiopathic(anti-ID) antibodies, single-chain antibodies, Fab fragments, F(ab′)fragments, fusion protein antibody fragments, immunoglobulin fragments,F_(v) fragments, single chain F_(v) fragments, and chimeras comprisingan immunoglobulin sequence and any modifications of the foregoing thatcomprise an antigen recognition site of the required selectivity.

“Autoimmune disease” encompasses any disease, as defined herein,resulting from an immune response against substances and tissuesnormally present in the body. Examples of suspected or known autoimmunediseases include rheumatoid arthritis, juvenile idiopathic arthritis,seronegative spondyloarthropathies, ankylosing spondylitis, psoriaticarthritis, antiphospholipid antibody syndrome, autoimmune hepatitis,Behçet's disease, bullous pemphigoid, coeliac disease, Crohn's disease,dermatomyositis, Goodpasture's syndrome, Graves' disease, Hashimoto'sdisease, idiopathic thrombocytopenic purpura, IgA nephropathy, Kawasakidisease, systemic lupus erythematosus, mixed connective tissue disease,multiple sclerosis, myasthenia gravis, polymyositis, primary biliarycirrhosis, psoriasis, scleroderma, Sjögren's syndrome, ulcerativecolitis, vasculitis, Wegener's granulomatosis, temporal arteritis,Takayasu's arteritis, Henoch-Schonlein purpura, leucocytoclasticvasculitis, polyarteritis nodosa, Churg-Strauss Syndrome, and mixedcryoglobulinemic vasculitis.

“Biomarker,” “biomarkers,” “marker” or “markers” in the context of thepresent teachings encompasses, without limitation, cytokines,chemokines, growth factors, proteins, peptides, nucleic acids,oligonucleotides, and metabolites, together with their relatedmetabolites, mutations, isoforms, variants, polymorphisms,modifications, fragments, subunits, degradation products, elements, andother analytes or sample-derived measures. Biomarkers can also includemutated proteins, mutated nucleic acids, variations in copy numbersand/or transcript variants. Biomarkers also encompass non-blood bornefactors and non-analyte physiological markers of health status, and/orother factors or markers not measured from samples (e.g., biologicalsamples such as bodily fluids), such as clinical parameters andtraditional factors for clinical assessments. Biomarkers can alsoinclude any indices that are calculated and/or created mathematically.Biomarkers can also include combinations of any one or more of theforegoing measurements, including temporal trends and differences.

A “clinical assessment,” or “clinical datapoint” or “clinical endpoint,”in the context of the present teachings can refer to a measure ofdisease activity or severity. A clinical assessment can include a score,a value, or a set of values that can be obtained from evaluation of asample (or population of samples) from a subject or subjects underdetermined conditions. A clinical assessment can also be a questionnairecompleted by a subject. A clinical assessment can also be predicted bybiomarkers and/or other parameters. One of skill in the art willrecognize that the clinical assessment for RA, as an example, cancomprise, without limitation, one or more of the following: DAS, DAS28,DAS28-ESR, DAS28-CRP, HAQ, mHAQ, MDHAQ, physician global assessment VAS,patient global assessment VAS, pain VAS, fatigue VAS, overall VAS, sleepVAS, SDAI, CDAI, RAPID3, RAPID4, RAPID5, ACR20, ACR50, ACR70, SF-36 (awell-validated measure of general health status), RA MRI score (RAMRIS;or RA MRI scoring system), total Sharp score (TSS), van derHeijde-modified TSS, van der Heijde-modified Sharp score (or Sharp-vander Heijde score (SHS)), Larsen score, TJC, swollen joint count (SJC),CRP titer (or level), and ESR.

The term “clinical parameters” in the context of the present teachingsencompasses all measures of the health status of a subject. A clinicalparameter can be used to derive a clinical assessment of the subject'sdisease activity. Clinical parameters can include, without limitation:therapeutic regimen (including but not limited to DMARDs, whetherconventional or biologics, steroids, etc.), TJC, SJC, morning stiffness,arthritis of three or more joint areas, arthritis of hand joints,symmetric arthritis, rheumatoid nodules, radiographic changes and otherimaging, gender/sex, age, race/ethnicity, disease duration, diastolicand systolic blood pressure, resting heart rate, height, weight,body-mass index, family history, CCP status (i.e., whether subject ispositive or negative for anti-CCP antibody), CCP titer, RF status, RFtiter, ESR, CRP titer, menopausal status, and whether asmoker/non-smoker.

“Clinical assessment” and “clinical parameter” are not mutuallyexclusive terms. There may be overlap in members of the two categories.For example, CRP titer can be used as a clinical assessment of diseaseactivity; or, it can be used as a measure of the health status of asubject, and thus serve as a clinical parameter.

The term “computer” carries the meaning that is generally known in theart; that is, a machine for manipulating data according to a set ofinstructions. For illustration purposes only, FIG. 16 is a high-levelblock diagram of a computer (1600). As is known in the art, a “computer”can have different and/or other components than those shown in FIG. 16.In addition, the computer 1600 can lack certain illustrated components.Moreover, the storage device (1608) can be local and/or remote from thecomputer (1600) (such as embodied within a storage area network (SAN)).As is known in the art, the computer (1600) is adapted to executecomputer program modules for providing functionality described herein.As used herein, the term “module” refers to computer program logicutilized to provide the specified functionality. Thus, a module can beimplemented in hardware, firmware, and/or software. In one embodiment,program modules are stored on the storage device (1608), loaded into thememory (1606), and executed by the processor (1602). Embodiments of theentities described herein can include other and/or different modulesthan the ones described here. In addition, the functionality attributedto the modules can be performed by other or different modules in otherembodiments. Moreover, this description occasionally omits the term“module” for purposes of clarity and convenience.

The term “cytokine” in the present teachings refers to any substancesecreted by specific cells of the immune system that carries signalslocally between cells and thus has an effect on other cells. The term“cytokines” encompasses “growth factors.” “Chemokines” are alsocytokines. They are a subset of cytokines that are able to inducechemotaxis in cells; thus, they are also known as “chemotacticcytokines.”

“DAIMRK” in the present teachings refers to a specific group, set orpanel of biomarkers, as the term “biomarkers” is defined herein. Wherethe biomarkers of certain embodiments of the present teachings areproteins, the gene symbols and names used herein are to be understood torefer to the protein products of these genes, and the protein productsof these genes are intended to include any protein isoforms of thesegenes, whether or not such isoform sequences are specifically describedherein. Where the biomarkers are nucleic acids, the gene symbols andnames used herein are to refer to the nucleic acids (DNA or RNA) ofthese genes, and the nucleic acids of these genes are intended toinclude any transcript variants of these genes, whether or not suchtranscript variants are specifically described herein. The DAIMRK groupof the present teachings is the group consisting of: apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); calprotectin; chemokine (C-Cmotif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage glycoprotein-39)(CHI3L1, or YKL-40); C-reactive protein, pentraxin-related (CRP);epidermal growth factor (beta-urogastrone) (EGF); intercellular adhesionmolecule 1 (ICAM1); ICTP; interleukin 18 (interferon-gamma-inducingfactor) (IL18); interleukin 1, beta (IL1B); interleukin 1 receptorantagonist (IL1RN); interleukin 6 (interferon, beta 2) (IL6);interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan sulfate, orKS; leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase)(MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3);pyridinoline (cross-links formed in collagen, derived from three lysineresidues), which may be referred to herein as PYD; resistin (RETN);serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily,member 1A (TNFRSF1A); tumor necrosis factor (ligand) superfamily, member13b (TNFSF13B, or BAFF); vascular cell adhesion molecule 1 (VCAM1); and,vascular endothelial growth factor A (VEGFA).

Calprotectin is a heteropolymer, comprising two protein subunits of genesymbols S100A8 and S100A9. ICTP is the carboxyterminal telopeptideregion of type I collagen, and is liberated during the degradation ofmature type I collagen. Type I collagen is present as fibers in tissue;in bone, the type I collagen molecules are crosslinked. The ICTP peptideis immunochemically intact in blood. (For the type I collagen gene, seeofficial symbol COL1A1, HUGO Gene Nomenclature Committee; also known asOI4; alpha 1 type I collagen; collagen alpha 1 chain type I; collagen ofskin, tendon and bone, alpha-1 chain; and, pro-alpha-1 collagen type 1).Keratan sulfate (KS, or keratosulfate) is not the product of a discretegene, but refers to any of several sulfated glycosaminoglycans. They aresynthesized in the central nervous system, and are found especially incartilage and bone. Keratan sulfates are large, highly hydratedmolecules, which in joints can act as a cushion to absorb mechanicalshock.

“DAS” refers to the Disease Activity Score, a measure of the activity ofRA in a subject, well-known to those of skill in the art. See D. van derHeijde et al., Ann. Rheum. Dis. 1990, 49(11):916-920. “DAS” as usedherein refers to this particular Disease Activity Score. The “DAS28”involves the evaluation of 28 specific joints. It is a current standardwell-recognized in research and clinical practice. Because the DAS28 isa well-recognized standard, it is often simply referred to as “DAS.”Unless otherwise specified, “DAS” herein will encompass the DAS28. ADAS28 can be calculated for an RA subject according to the standard asoutlined at the das-score.nl website, maintained by the Department ofRheumatology of the University Medical Centre in Nijmegen, theNetherlands. The number of swollen joints, or swollen joint count out ofa total of 28 (SJC28), and tender joints, or tender joint count out of atotal of 28 (TJC28) in each subject is assessed. In some DAS28calculations the subject's general health (GH) is also a factor, and canbe measured on a 100 mm Visual Analogue Scale (VAS). OH may also bereferred to herein as PG or PGA, for “patient global health assessment”(or merely “patient global assessment”). A “patient global healthassessment VAS,” then, is GH measured on a Visual Analogue Scale.

“DAS28-CRP” (or “DAS28CRP”) is a DAS28 assessment calculated using CRPin place of ESR (see below). CRP is produced in the liver. Normallythere is little or no CRP circulating in an individual's blood serum—CRPis generally present in the body during episodes of acute inflammationor infection, so that a high or increasing amount of CRP in blood serumcan be associated with acute infection or inflammation. A blood serumlevel of CRP greater than 1 mg/dL is usually considered high. Mostinflammation and infections result in CRP levels greater than 10 mg/dL.The amount of CRP in subject sera can be quantified using, for example,the DSL-10-42100 ACTIVE® US C-Reactive Protein Enzyme-LinkedImmunosorbent Assay (ELISA), developed by Diagnostics SystemsLaboratories, Inc. (Webster, Tex.). CRP production is associated withradiological progression in RA. See M. Van Leeuwen et al., Br. J. Rheum.1993, 32(suppl.):9-13). CRP is thus considered an appropriatealternative to ESR in measuring RA disease activity. See R. Mallya etal., J. Rheum. 1982, 9(2):224-228, and F. Wolfe, J. Rheum. 1997,24:1477-1485.

The DAS28-CRP can be calculated according to either of the formulasbelow, with or without the GH factor, where “CRP” represents the amountof this protein present in a subject's blood serum in mg/L, “sqrt”represents the square root, and “In” represents the natural logarithm:

DAS28-CRP with GH (orDAS28-CRP4)=(0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1))+(0.014*GH)+0.96;or,  (a)

DAS28-CRP without GH (orDAS28-CRP3)=(0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1))*1.10+1.15.  (b)

The “DAS28-ESR” is a DAS28 assessment wherein the ESR for each subjectis also measured (in mm/hour). The DAS28-ESR can be calculated accordingto the formula:

DAS28-ESR with GH (orDAS28-ESR4)=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.70*ln(ESR)+0.014*GH;or,  (a)

DAS28-ESR withoutGH=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.70*ln(ESR)*1.08+0.16.  (b)

Unless otherwise specified herein, the term “DAS28,” as used in thepresent teachings, can refer to a DAS28-ESR or DAS28-CRP, as obtained byany of the four formulas described above; or, DAS28 can refer to anotherreliable DAS28 formula as may be known in the art.

A “dataset” is a set of numerical values resulting from evaluation of asample (or population of samples) under a desired condition. The valuesof the dataset can be obtained, for example, by experimentally obtainingmeasures from a sample and constructing a dataset from thesemeasurements; or alternatively, by obtaining a dataset from a serviceprovider such as a laboratory, or from a database or a server on whichthe dataset has been stored.

In certain embodiments of the present teachings, a dataset of values isdetermined by measuring at least two biomarkers from the DAIMRK group.This dataset is used by an interpretation function according to thepresent teachings to derive a DAI score (see definition, “DAI score,”below), which provides a quantitative measure of inflammatory diseaseactivity in a subject. In the context of RA, the DAI score thus derivedfrom this dataset is also useful in predicting a DAS28 score, with ahigh degree of association, as is shown in the Examples below. The atleast two markers can comprise: (APOA1 and IL8), (Calprotectin and CRP),(Calprotectin and EGF), (Calprotectin and IL8), (CRP and APOA1), (CRPand APOC3), (CRP and CCL22), (CRP and CHI3L), (CRP and EGF), (CRP andICAM1), (CRP and IL1B), (CRP and IL6), (CRP and IL6R), (CRP and IL8),(CRP and LEP), (CRP and MMP1), (CRP and MMP3), (CRP and RETN), (CRP andSAA1), (CRP and TNFRSF1A), (CRP and VCAM1), (CRP and VEGF), (EGF andAPOA1), (EGF and CHI3L1), (EGF and ICAM1), (EGF and IL8), (EGF and LEP),(EGF and MMP1), (EGF and TNFRSF1A), (EGF and VCAM1), (ICAM1 and IL8),(IL1RN and CRP), (IL1RN and EGF), (IL1RN and IL8), (IL8 and APOC3), (ILand CCL22), (IL8 and CHI3L1), (IL8 and IL6), (IL8 and IL6R), (IL8 andTNFRSF1A), (LEP and IL8), (MMP3 and IL8), (RETN and IL8), (SAA1 andEGF), (SAA1 and IL8), (SAA1 and LEP), (SAA1 and REIN), or (VCAM1 andIL8). The at least two markers can also comprise (calprotectin andCHI3L1), (calprotectin and interleukin), (calprotectin and LEP),(calprotectin and pyridinoline), (calprotectin and RETN), (CCL22 andcalprotectin), (CCL22 and CRP), (CCL22 and 1A6), (CCL22 and SAA1), (CRPand calprotectin), (CRP and CHI3L1), (CRP and EGF), (CRP and ICAM1),(CRP and IL1B), (CRP and IL1RN), (CRP and IL6), (CRP and IL6R), (CRP andIL8), (CRP and LEP), (CRP and MMP1), (CRP and MMP3), (CRP andpyridinoline), (CRP and RETN), (CRP and SAA1), (CRP and TNFRSF1A), (CRPand VCAM1), (CRP and VEGFA), (EGF and calprotectin), (EGF and L6), (EGFand SAA1), (ICAM1 and calprotectin), (ICAM1 and IL6), (ICAM1 and SAA1),(IL1B and calprotectin), (IL1B and IL6), (IL1B and MMP3), (IL1B andSAA1), (IL6 and calprotectin), (IL6 and CHI3L1), (IL6 and IL1RN), (IL6and IL8), (IL6 and LEP), (IL6 and MMP1), (L6 and MMP3), (IL6 andpyridinoline), (IL6 and RETN), (IL6 and SAA1), (IL6 and TNFRSF1A), (IL6and VCAM1), (IL6 and VEGFA), (IL6R and calprotectin), (IL6R and IL6),(IL6R and SAA1), (IL8 and calprotectin), (IL8 and MMP3), (IL8 and SAA1),(MMP1 and calprotectin), (MMP1 and SAA1), (MMP3 and calprotectin), (MMP3and CHI3L1), (MMP3 and SAA1), (SAA1 and calprotectin), (SAA1 andCHI3L1), (SAA1 and IL1RN), (SAA1 and LEP), (SAA1 and pyridinoline),(SAA1 and RETN), (SAA1 and TNFRSF1A), (SAA1 and VCAM1), (SAA1 andVEGFA), (TNFRSF1A and calprotectin), (VCAM1 and calprotectin); or,(VEGFA and calprotectin).

The term “disease” in the context of the present teachings encompassesany disorder, condition, sickness, ailment, etc. that manifests in,e.g., a disordered or incorrectly functioning organ, part, structure, orsystem of the body, and results from, e.g., genetic or developmentalerrors, infection, poisons, nutritional deficiency or imbalance,toxicity, or unfavorable environmental factors.

A “disease activity index score,” “DAI score,” or simply “DAI,” in thecontext of the present teachings, is a score that provides aquantitative measure of inflammatory disease activity or the state ofinflammatory disease in a subject. A set of data from particularlyselected biomarkers, such as markers from the DAIMRK or ALLMRK set, isinput into an interpretation function according to the present teachingsto derive the DAI score. The interpretation function, in someembodiments, can be created from predictive or multivariate modelingbased on statistical algorithms. Input to the interpretation functioncan comprise the results of testing two or more of the DAIMRK or ALLMRKset of biomarkers, alone or in combination with clinical parametersand/or clinical assessments, also described herein. In some embodimentsof the present teachings, the DAI score is a quantitative measure ofautoimmune disease activity. In some embodiments, the DAI score is aquantitative measure of RA disease activity.

A DMARD can be conventional or biologic. Examples of DMARDs that aregenerally considered conventional include, but are not limited to, MTX,azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), ciclosporin(CSA, or cyclosporine, or cyclosporin), doxycycline (DOXY),hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide(LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Examples of otherconventional DMARDs include, but are not limited to, folinic acid,D-pencillamine, gold auranofin, gold aurothioglucose, gold thiomalate,cyclophosphamide, and chlorambucil. Examples of biologic DMARDs (orbiologic drugs) include but are not limited to biological agents thattarget the tumor necrosis factor (TNF)-alpha molecules and the TNFinhibitors, such as infliximab, adalimumab, etanercept and golimumab.Other classes of biologic DMARDs include IL1 inhibitors such asanakinra, T-cell modulators such as abatacept, B-cell modulators such asrituximab, and IL6 inhibitors such as tocilizumab.

“Inflammatory disease” in the context of the present teachingsencompasses, without limitation, any disease, as defined herein,resulting from the biological response of vascular tissues to harmfulstimuli, including but not limited to such stimuli as pathogens, damagedcells, irritants, antigens and, in the case of autoimmune disease,substances and tissues normally present in the body. Examples ofinflammatory disease include RA, atherosclerosis, asthma, autoimmunediseases, chronic inflammation, chronic prostatitis, glomerulonephritis,hypersensitivities, inflammatory bowel diseases, pelvic inflammatorydisease, reperfusion injury, transplant rejection, and vasculitis.

“Interpretation function,” as used herein, means the transformation of aset of observed data into a meaningful determination of particularinterest; e.g., an interpretation function may be a predictive modelthat is created by utilizing one or more statistical algorithms totransform a dataset of observed biomarker data into a meaningfuldetermination of disease activity or the disease state of a subject.

“Measuring” or “measurement” in the context of the present teachingsrefers to determining the presence, absence, quantity, amount, oreffective amount of a substance in a clinical or subject-derived sample,including the concentration levels of such substances, or evaluating thevalues or categorization of a subject's clinical parameters.

“Performance” in the context of the present teachings relates to thequality and overall usefulness of, e.g., a model, algorithm, ordiagnostic or prognostic test. Factors to be considered in model or testperformance include, but are not limited to, the clinical and analyticalaccuracy of the test, use characteristics such as stability of reagentsand various components, ease of use of the model or test, health oreconomic value, and relative costs of various reagents and components ofthe test.

A “population” is any grouping of subjects of like specifiedcharacteristics. The grouping could be according to, for example butwithout limitation, clinical parameters, clinical assessments,therapeutic regimen, disease status (e.g. with disease or healthy),level of disease activity, etc. In the context of using the DAI score incomparing disease activity between populations, an aggregate value canbe determined based on the observed DAI scores of the subjects of apopulation; e.g., at particular timepoints in a longitudinal study. Theaggregate value can be based on, e.g., any mathematical or statisticalformula useful and known in the art for arriving at a meaningfulaggregate value from a collection of individual datapoints; e.g., mean,median, median of the mean, etc.

A “predictive model,” which term may be used synonymously herein with“multivariate model” or simply a “model,” is a mathematical constructdeveloped using a statistical algorithm or algorithms for classifyingsets of data. The term “predicting” refers to generating a value for adatapoint without actually performing the clinical diagnostic proceduresnormally or otherwise required to produce that datapoint; “predicting”as used in this modeling context should not be understood solely torefer to the power of a model to predict a particular outcome.Predictive models can provide an interpretation function; e.g., apredictive model can be created by utilizing one or more statisticalalgorithms or methods to transform a dataset of observed data into ameaningful determination of disease activity or the disease state of asubject. See Calculation of the DAI score for some examples ofstatistical tools useful in model development.

A “prognosis” is a prediction as to the likely outcome of a disease.Prognostic estimates are useful in, e.g., determining an appropriatetherapeutic regimen for a subject.

A “quantitative dataset,” as used in the present teachings, refers tothe data derived from, e.g., detection and composite measurements of aplurality of biomarkers (i.e., two or more) in a subject sample. Thequantitative dataset can be used in the identification, monitoring andtreatment of disease states, and in characterizing the biologicalcondition of a subject. It is possible that different biomarkers will bedetected depending on the disease state or physiological condition ofinterest.

A “sample” in the context of the present teachings refers to anybiological sample that is isolated from a subject. A sample can include,without limitation, a single cell or multiple cells, fragments of cells,an aliquot of body fluid, whole blood, platelets, serum, plasma, redblood cells, white blood cells or leucocytes, endothelial cells, tissuebiopsies, synovial fluid, lymphatic fluid, ascites fluid, andinterstitial or extracellular fluid. The term “sample” also encompassesthe fluid in spaces between cells, including gingival crevicular fluid,bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, semen,sweat, urine, or any other bodily fluids. “Blood sample” can refer towhole blood or any fraction thereof, including blood cells, red bloodcells, white blood cells or leucocytes, platelets, serum and plasma.Samples can be obtained from a subject by means including but notlimited to venipuncture, excretion, ejaculation, massage, biopsy, needleaspirate, lavage, scraping, surgical incision, or intervention or othermeans known in the art.

A “score” is a value or set of values selected so as to provide aquantitative measure of a variable or characteristic of a subject'scondition, and/or to discriminate, differentiate or otherwisecharacterize a subject's condition. The value(s) comprising the scorecan be based on, for example, a measured amount of one or more sampleconstituents obtained from the subject, or from clinical parameters, orfrom clinical assessments, or any combination thereof. In certainembodiments the score can be derived from a single constituent,parameter or assessment, while in other embodiments the score is derivedfrom multiple constituents, parameters and/or assessments. The score canbe based upon or derived from an interpretation function; e.g., aninterpretation function derived from a particular predictive model usingany of various statistical algorithms known in the art. A “change inscore” can refer to the absolute change in score, e.g. from onetimepoint to the next, or the percent change in score, or the change inthe score per unit time (i.e., the rate of score change).

“Statistically significant” in the context of the present teachingsmeans an observed alteration is greater than what would be expected tooccur by chance alone (e.g., a “false positive”). Statisticalsignificance can be determined by any of various methods well-known inthe art. An example of a commonly used measure of statisticalsignificance is the p-value. The p-value represents the probability ofobtaining a given result equivalent to a particular datapoint, where thedatapoint is the result of random chance alone. A result is oftenconsidered highly significant (not random chance) at a p-value less thanor equal to 0.05.

A “subject” in the context of the present teachings is generally amammal. The subject can be a patient. The term “mammal” as used hereinincludes but is not limited to a human, non-human primate, dog, cat,mouse, rat, cow, horse, and pig. Mammals other than humans can beadvantageously used as subjects that represent animal models ofinflammation. A subject can be male or female. A subject can be one whohas been previously diagnosed or identified as having an inflammatorydisease. A subject can be one who has already undergone, or isundergoing, a therapeutic intervention for an inflammatory disease. Asubject can also be one who has not been previously diagnosed as havingan inflammatory disease; e.g., a subject can be one who exhibits one ormore symptoms or risk factors for an inflammatory condition, or asubject who does not exhibit symptoms or risk factors for aninflammatory condition, or a subject who is asymptomatic forinflammatory disease.

A “therapeutic regimen,” “therapy” or “treatment(s),” as describedherein, includes all clinical management of a subject and interventions,whether biological, chemical, physical, or a combination thereof,intended to sustain, ameliorate, improve, or otherwise alter thecondition of a subject. These terms may be used synonymously herein.Treatments include but are not limited to administration ofprophylactics or therapeutic compounds (including conventional DMARDs,biologic DMARDs, non-steroidal anti-inflammatory drugs (NSAID's) such asCOX-2 selective inhibitors, and corticosteroids), exercise regimens,physical therapy, dietary modification and/or supplementation, bariatricsurgical intervention, administration of pharmaceuticals and/oranti-inflammatories (prescription or over-the-counter), and any othertreatments known in the art as efficacious in preventing, delaying theonset of, or ameliorating disease. A “response to treatment” includes asubject's response to any of the above-described treatments, whetherbiological, chemical, physical, or a combination of the foregoing. A“treatment course” relates to the dosage, duration, extent, etc. of aparticular treatment or therapeutic regimen.

Use of the Present Teachings in the Diagnosis and Prognosis of Disease

In some embodiments of the present teachings, biomarkers selected fromthe DAIMARK or ALLMRK group can be used in the derivation of a DAIscore, as described herein, which DAI score can be used to providediagnosis, prognosis and monitoring of disease state and/or diseaseactivity in inflammatory disease and in autoimmune disease. In certainembodiments, the DAI score can be used to provide diagnosis, prognosisand monitoring of disease state and/or disease activity of RA.

Identifying the state of inflammatory disease in a subject allows for aprognosis of the disease, and thus for the informed selection of,initiation of, adjustment of or increasing or decreasing varioustherapeutic regimens in order to delay, reduce or prevent that subject'sprogression to a more advanced disease state. In some embodiments,therefore, subjects can be identified as having a particular level ofinflammatory disease activity and/or as being at a particular state ofdisease, based on the determination of their DAI scores, and so can beselected to begin or accelerate treatment, as treatment is definedherein, to prevent or delay the further progression of inflammatorydisease. In other embodiments, subjects that are identified via theirDAI scores as having a particular level of inflammatory diseaseactivity, and/or as being at a particular state of inflammatory disease,can be selected to have their treatment decreased or discontinued, whereimprovement or remission in the subject is seen.

Blood-based biomarkers that report on the current rate of jointdestructive processes could also present a powerful prognostic approachto identifying subjects at highest risk of accelerated bone andcartilage damage. In some embodiments of the present teachings,biomarkers from the DAIMRK or ALLMRK group can be measured fromsubjects' or a subject's samples obtained at various time points (e.g.,longitudinally), to obtain a series of DAI scores, and the scores canthen be associated with radiological results (such as, e.g., thoseobtained by TSS) at various time points and so provide a measurement ofdisease progression. See Example 2. The association of the DAI scoreswith, e.g., change of TSS results can be analyzed statistically forcorrelation (e.g., Spearman correlation) using multivariate analysis tocreate single time point or longitudinal hierarchical linear models andensure accuracy. Serum biomarkers of the DAIMRK or ALLMRK group can thusbe used as an alternative to US/radiological results in estimating ratesof progression of disease, and predicting joint damage in RA. Predictivemodels using biomarkers can thus identify subjects who need moreaggressive treatment, and earlier, and can thereby improve subjectoutcomes. In other embodiments, the DAI scores from one subject can becompared with each other, for observations of longitudinal trending asan effect of, e.g., choice or effectiveness of therapeutic regimen, oras a result of the subject's response to treatment regimens, or acomparison of the subject's responses to different regimens.

The present teachings indicate that DAIMRK- or ALLMRK-derived formulasdeveloped in cross-sectional analysis are a strong predictor of diseaseactivity over time; e.g., longitudinally. See Example 2. This is asignificant finding from a clinical care perspective. Currently no testsare available to accurately measure and track RA disease activity overtime in the clinic. Several recent studies have demonstrated thatoptimal treatment intervention can dramatically improve clinicaloutcomes. See Y P M Goekoop-Ruitenman et al., Ann. Rheum. Dis. 2009(Epublication Jan. 20, 2009); C. Grigor et al., Lancet 2004,364:263-269; S M M Verstappen et al., Ann. Rheum. Dis. 2007,66:1443-1449. In these studies disease activity levels are frequentlymonitored and treatment is increased in nonremission subjects. Thisconcept of treating to remission has been denoted, “Tight Control.”Numbers of subjects achieving low disease activity and remission inTight Control trials is high. In addition, Tight Control cohorts achievedramatically improved outcomes relative to cohorts receiving standard ofcare in clinical practice, where remission is less achievable. This isin part due to a lack of easy and sensitive tools to quantitativelymonitor disease activity in a real-world clinical practice. Monitoringin these controlled trials is via clinical trial measures, such as DASand Sharp Scores changes, which are not widely practiced in thereal-world clinical setting. The tests developed from variousembodiments of the present teachings will facilitate the monitoring ofdisease activity and Tight Control practices, and result in improvedcontrol of disease activity and improved clinical outcomes.

In regards to the need for early and accurate diagnosis of RA, recentadvances in RA treatment provide a means for more profound diseasemanagement and optimal treatment of RA within the first months ofsymptom onset, which in turn result in significantly improved outcomes.See F. Wolfe, Arth. Rheum. 2000, 43(12):2751-2761; M. Matucci-Cerinic,Clin. Exp. Rheum. 2002, 20(4):443-444; and, V. Nell et. al., Lancet2005, 365(9455):199-200. Unfortunately, most subjects do not receiveoptimal treatment within this narrow window of opportunity, resulting inpoorer outcomes and irreversible joint damage, in part because of thelimits of current diagnostic laboratory tests. Numerous difficultiesexist in diagnosing RA subject. This is in part because at their earlystages, symptoms may not be fully differentiated. It is also becausediagnostic tests for RA were developed based on phenomenologicalfindings, not the biological basis of disease. In various embodiments ofthe present teachings, multi-biomarker algorithms can be derived frombiomarkers of the DAIMRK set, which have diagnostic potential. SeeExample 4. This aspect of the present teachings has the potential toimprove both the accuracy of RA diagnosis, and the speed of detection ofRA.

Rating Disease Activity

In some embodiments of the present teachings, the DAI score, derived asdescribed herein, can be used to rate inflammatory disease activity;e.g., as high, medium or low. In some embodiments of the presentteachings, autoimmune disease activity can be so rated. In otherembodiments, RA disease activity can be so rated. Using RA disease as anexample, because the DAI score correlates well and with high accuracywith clinical assessments of RA (e.g., with the DAS28 score), DAIcut-off scores can be set at predetermined levels to indicate levels ofRA disease activity, and to correlate with the cut-offs traditionallyestablished for rating RA activity via DAS28 scores. See Example 3.Because the DAI score correlates well with traditional clinicalassessments of inflammatory disease activity, e.g. in RA, in otherembodiments of the present teachings bone damage itself in a subject orpopulation, and thus disease progression, can be tracked via the use andapplication of the DAI score.

These properties of the DAIMRK set of biomarkers can be used for severalpurposes. On a subject-specific basis, they provide a context forunderstanding the relative level of disease activity. The DAIMRK-basedrating of disease activity can be used, e.g., to guide the clinician indetermining treatment, in setting a treatment course, and/or to informthe clinician that the subject is in remission. Moreover, it provides ameans to more accurately assess and document the qualitative level ofdisease activity in a subject. It is also useful from the perspective ofassessing clinical differences among populations of subjects within apractice. For example, this tool can be used to assess the relativeefficacy of different treatment modalities. Moreover, it is also usefulfrom the perspective of assessing clinical differences among differentpractices. This would allow physicians to determine what global level ofdisease control is achieved by their colleagues, and/or for healthcaremanagement groups to compare their results among different practices forboth cost and comparative effectiveness.

Subject Screening

Certain embodiments of the present teachings can also be used to screensubject populations in any number of settings. For example, a healthmaintenance organization, public health entity or school health programcan screen a group of subjects to identify those requiringinterventions, as described above. Other embodiments of these teachingscan be used to collect disease activity data on one or more populationsof subjects, to identify subject disease status in the aggregate, inorder to, e.g., determine the effectiveness of the clinical managementof a population, or determine gaps in clinical management. Insurancecompanies (e.g., health, life, or disability) may request the screeningof applicants in the process of determining coverage for possibleintervention. Data collected in such population screens, particularlywhen tied to any clinical progression to conditions such as inflammatorydisease and RA, will be of value in the operations of, for example,health maintenance organizations, public health programs and insurancecompanies.

Such data arrays or collections can be stored in machine-readable mediaand used in any number of health-related data management systems toprovide improved healthcare services, cost-effective healthcare, andimproved insurance operation, among other things. See, e.g., U.S. PatentApplication No. 2002/0038227; U.S. Patent Application No. 2004/0122296;U.S. Patent Application No. 2004/0122297; and U.S. Pat. No. 5,018,067.Such systems can access the data directly from internal data storage orremotely from one or more data storage sites as further detailed herein.Thus, in a health-related data management system, wherein it isimportant to manage inflammatory disease progression for a population inorder to reduce disease-related employment productivity loss, disabilityand surgery, and thus reduce healthcare costs in the aggregate, variousembodiments of the present teachings provide an improvement comprisingthe use of a data array encompassing the biomarker measurements asdefined herein, and/or the resulting evaluation of disease status andactivity from those biomarker measurements.

Measuring Accuracy and Performance of the Present Teachings

The performance of the present teachings can be assessed in any ofvarious ways. Assessing the performance of an embodiment of the presentteachings can provide a measurement of the accuracy of that embodiment,where, e.g., that embodiment is a predictive model, or a test, assay,method or procedure, whether diagnostic or prognostic. This accuracyassessment can relate to the ability of the predictive model or the testto determine the inflammatory disease activity status of a subject orpopulation. In other embodiments, the performance assessment relates tothe accuracy of the predictive model or test in distinguishing betweensubjects with or without inflammatory disease. In other embodiments, theassessment relates to the accuracy of the predictive model or test indistinguishing between states of inflammatory disease in one subject atdifferent time points.

The distinguishing ability of the predictive model or test can be basedon whether the subject or subjects have a significant alteration in thelevels of one or more biomarkers. In some embodiments a significantalteration, in the context of the present teachings, can mean that themeasurement of the biomarkers, as represented by the DAI score computedby the DAI formula as generated by the predictive model, is differentthan some predetermined DAI cut-off point (or threshold value) for thosebiomarkers when input to the DAI formula as described herein. Thissignificant alteration in biomarker levels as reflected in differing DAIscores can therefore indicate that the subject has inflammatory disease,or is at a particular state or severity of inflammatory disease. Thedifference in the levels of biomarkers between the subject and normal,in those embodiments when such comparisons are done, is preferablystatistically significant, and can be an increase in biomarker level orlevels, or a decrease in biomarker level or levels. In some embodimentsof the present teachings, a significant alteration can mean that a DAIscore is derived from measuring the levels of one or more biomarkers,and this score alone, without comparison to some predetermined cut-offpoint (or threshold value) for those biomarkers, indicates that thesubject has inflammatory disease or has a particular state ofinflammatory disease. Further, achieving increased analytical andclinical accuracy may require that combinations of two or morebiomarkers be used together in panels, and combined with mathematicalalgorithms derived from predictive models to obtain the DAI score.

Use of statistical values such as the AUC, and specifically the AUC asit relates to the ROC curve, encompassing all potential threshold orcut-off point values is generally used to quantify predictive modelperformance. Acceptable degrees of accuracy can be defined. In certainembodiments of the present teachings, an acceptable degree of accuracycan be one in which the AUC for the ROC curve is 0.60 or higher.

In general, defining the degree of accuracy for the relevant predictivemodel or test (e.g., cut-off points on a ROC curve), defining anacceptable AUC value, and determining the acceptable ranges in relativeconcentration of what constitutes an effective amount of the biomarkersof the present teachings, allows one of skill in the art to use thebiomarkers of the present teachings to identify inflammatory diseaseactivity in subjects or populations with a pre-determined level ofpredictability and performance.

In various embodiments of the present teachings, measurements frommultiple biomarkers, such as those of the DAIMRK set, can be combinedinto a single value, the DAI score, using various statistical analysesand modeling techniques as described herein. Because the DAI scoredemonstrates strong association with established disease activityassessments, such as the DAS28, the DAI score can provide a quantitativemeasure for monitoring the extent of subject disease activity, andresponse to treatment. Example 1 below, e.g., demonstrates that DAIscores are strongly associated with DAS28; thus, DAI provides anaccurate quantitative measure of subject disease activity. See also FIG.1 et seq., in which are shown DAI scores based on sets of biomarkers,which scores demonstrate a strong association with DAS28-CRP, asevidenced by the AUC values shown (e.g., greater than or equal to 0.65).

Calculation of the DAI Score

In some embodiments of the present teachings, inflammatory diseaseactivity in a subject is measured by: determining the levels ininflammatory disease subject serum of two or more biomarkers selectedfrom the DAIMRK set, then applying an interpretation function totransform the biomarker levels into a single DAI score, which provides aquantitative measure of inflammatory disease activity in the subject,correlating well with traditional clinical assessments of inflammatorydisease activity (e.g., a DAS28 or CDAI score in RA), as is demonstratedin the Examples below. In some embodiments, the disease activity someasured relates to an autoimmune disease. In some embodiments, thedisease activity so measured relates to RA.

In some embodiments, the interpretation function is based on apredictive model. Established statistical algorithms and methodswell-known in the art, useful as models or useful in designingpredictive models, can include but are not limited to: analysis ofvariants (ANOVA); Bayesian networks; boosting and Ada-boosting;bootstrap aggregating (or bagging) algorithms; decision treesclassification techniques, such as Classification and Regression Trees(CART), boosted CART, Random Forest (RF), Recursive Partitioning Trees(RPART), and others; Curds and Whey (CW); Curds and Whey-Lasso;dimension reduction methods, such as principal component analysis (PCA)and factor rotation or factor analysis; discriminant analysis, includingLinear Discriminant Analysis (LDA), Eigengene Linear DiscriminantAnalysis (ELDA), and quadratic discriminant analysis; DiscriminantFunction Analysis (DFA); factor rotation or factor analysis; geneticalgorithms; Hidden Markov Models; kernel based machine algorithms suchas kernel density estimation, kernel partial least squares algorithms,kernel matching pursuit algorithms, kernel Fisher's discriminateanalysis algorithms, and kernel principal components analysisalgorithms; linear regression and generalized linear models, includingor utilizing Forward Linear Stepwise Regression, Lasso (or LASSO)shrinkage and selection method, and Elastic Net regularization andselection method; glmnet (Lasso and Elastic Net-regularized generalizedlinear model); Logistic Regression (LogReg); meta-learner algorithms;nearest neighbor methods for classification or regression, e.g.Kth-nearest neighbor (KNN); non-linear regression or classificationalgorithms; neural networks; partial least square; rules basedclassifiers; shrunken centroids (SC): sliced inverse regression;Standard for the Exchange of Product model data, Application InterpretedConstructs (StepAIC); super principal component (SPC) regression; and,Support Vector Machines (SVM) and Recursive Support Vector Machines(RSVM), among others. Additionally, clustering algorithms as are knownin the art can be useful in determining subject sub-groups.

Logistic Regression is the traditional predictive modeling method ofchoice for dichotomous response variables; e.g., treatment 1 versustreatment 2. It can be used to model both linear and non-linear aspectsof the data variables and provides easily interpretable odds ratios.

Discriminant Function Analysis (DFA) uses a set of analytes as variables(roots) to discriminate between two or more naturally occurring groups.DFA is used to test analytes that are significantly different betweengroups. A forward step-wise DFA can be used to select a set of analytesthat maximally discriminate among the groups studied. Specifically, ateach step all variables can be reviewed to determine which willmaximally discriminate among groups. This information is then includedin a discriminative function, denoted a root, which is an equationconsisting of linear combinations of analyte concentrations for theprediction of group membership. The discriminatory potential of thefinal equation can be observed as a line plot of the root valuesobtained for each group. This approach identifies groups of analyteswhose changes in concentration levels can be used to delineate profiles,diagnose and assess therapeutic efficacy. The DFA model can also createan arbitrary score by which new subjects can be classified as either“healthy” or “diseased.” To facilitate the use of this score for themedical community the score can be rescaled so a value of 0 indicates ahealthy individual and scores greater than 0 indicate increasing diseaseactivity.

Classification and regression trees (CART) perform logical splits(if/then) of data to create a decision tree. All observations that fallin a given node are classified according to the most common outcome inthat node. CART results are easily interpretable—one follows a series ofif/then tree branches until a classification results.

Support vector machines (SVM) classify objects into two or more classes.Examples of classes include sets of treatment alternatives, sets ofdiagnostic alternatives, or sets of prognostic alternatives. Each objectis assigned to a class based on its similarity to (or distance from)objects in the training data set in which the correct class assignmentof each object is known. The measure of similarity of a new object tothe known objects is determined using support vectors, which define aregion in a potentially high dimensional space (>R6).

The process of bootstrap aggregating, or “bagging,” is computationallysimple. In the first step, a given dataset is randomly resampled aspecified number of times (e.g., thousands), effectively providing thatnumber of new datasets, which are referred to as “bootstrappedresamples” of data, each of which can then be used to build a model.Then, in the example of classification models, the class of every newobservation is predicted by the number of classification models createdin the first step. The final class decision is based upon a “majorityvote” of the classification models; i.e., a final classification call isdetermined by counting the number of times a new observation isclassified into a given group, and taking the majority classification(33%+ for a three-class system). In the example of logistical regressionmodels, if a logistical regression is bagged 1000 times, there will be1000 logistical models, and each will provide the probability of asample belonging to class 1 or 2.

Curds and Whey (CW) using ordinary least squares (OLS) is anotherpredictive modeling method. See L Breiman and J H Friedman, J. RoyalStat. Soc. B 1997, 59(1):3-54. This method takes advantage of thecorrelations between response variables to improve predictive accuracy,compared with the usual procedure of performing an individual regressionof each response variable on the common set of predictor variables X. InCW, Y=XB*S, where Y=(y_(kj)) with k for the k^(th) patient and j forj^(th) response (j=1 for TJC, j=2 for SJC, etc.), B is obtained usingOLS, and S is the shrinkage matrix computed from the canonicalcoordinate system. Another method is Curds and Whey and Lasso incombination (CW-Lasso). Instead of using OLS to obtain B, as in CW, hereLasso is used, and parameters are adjusted accordingly for the Lassoapproach.

Many of these techniques are useful either combined with a biomarkerselection technique (such as, for example, forward selection, backwardsselection, or stepwise selection), or for complete enumeration of allpotential panels of a given size, or genetic algorithms, or they canthemselves include biomarker selection methodologies in their owntechniques. These techniques can be coupled with information criteria,such as Akaike's Information Criterion (AIC), Bayes InformationCriterion (BIC), or cross-validation, to quantify the tradeoff betweenthe inclusion of additional biomarkers and model improvement, and tominimize overfit. The resulting predictive models can be validated inother studies, or cross-validated in the study they were originallytrained in, using such techniques as, for example, Leave-One-Out (LOO)and 10-Fold cross-validation (10-Fold CV).

One example of an interpretation function that provides a DAI score,derived from a statistical modeling method as described above, is givenby the following function:

DAI=b ₀ +b ₁*DAIMRK₁ ^(x) −b ₂*DAIMRK₂ ^(x) −b ₃*DAIMRK₃ ^(x) . . . −b_(n)*DAIMRK_(n) ^(x);

where DAI is the DAI score, b_(0-n) are constants, and DAIMRK_(1-n) ^(x)are the serum concentrations to the x^(th) power of n differentbiomarkers selected from the DAIMRK panel. DAI scores thus obtained forRA subjects with known clinical assessments (e.g., DAS28 scores) canthen be compared to those known assessments to determine the level ofcorrelation between the two assessments, and hence determine theaccuracy of the DAI score and its underlying predictive model. SeeExamples below for specific formulas and constants.

More generally, the function can be described as: DAI=F(DAIMRK₁ ^(x),DAIMRK₂ ^(x), . . . , DAIMRK_(n) ^(x)) where DAI is the DAI score, F isthe function, and DAIMRK_(1-n) ^(x) are the serum concentrations to thex^(th) power of n different biomarkers selected from the DAIMRK panel.The function is described in the following paragraph.

An interpretation function for providing a DAI score can also be derivedbased on models built to predict components of a disease activityassessment, such as DAS28-CRP, rather than predicting disease activityentirely. See Example 11. An example of such a function is given by thefollowing, wherein biomarkers are used to provide improved predictedcomponents of the DAS score:

DAIscore=((0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*PPGA)+(0.36*ln(CRP/10⁶+1))+0.96)*10.53+1;

IPTJC=Improved PTJC=max(0.1739*PTJC+0.7865*PSJC,0);

IPSJC=Improved PSJC=max(0.1734*PTJC+0.7839*PSJC,0);

PTJC=Prediction of Tender JointCount=−38.564+3.997*(SAA1)^(1/10)+17.331*(IL6)^(1/10)+4.665*(CHI3L1)^(1/10)−15.236*(EGF)^(1/10)+2.651*(TNFRSF1A)^(1/10)+2.641*(LEP)^(1/10)+4.026*(VEGFA)^(1/10)−1.47*(VCAM1)^(1/10);

PSJC=Prediction of Swollen JointCount=−25.444+4.051*(SAA1)^(1/10)+16.154*(IL6)^(1/10)−11.847*(EGF)^(1/10)+3.091*(CHI3L1)^(1/10)+0.353*(TNFRSF1A)^(1/10);

PPGA=Prediction of Patient GlobalAssessment=−13.489+5.474*(IL6)^(1/10)+0.486*(SAA1)^(1/10)+2.246*(MMP1)^(1/10)+1.684*(leptin)^(1/10)+4.14*(TNFRSF1A)^(1/10)+2.292*(VEGFA)^(1/10)−1.898*(EGF)^(1/10)+0.028*(MMP3)^(1/10)−2.892*(VCAM1)^(1/10)−506*(RETN)^(1/10)

in which serum levels x for all biomarkers but CRP are transformed asx^(1/10) units for all biomarkers are in pg/mL, and n is natural log, orlog_(e).

Where CRP units are obtained in mg/L and other markers are pg/mL DAIscore=((0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(CRP+1))+0.96)*10.53+1.

It is understood that if biomarkers are measured in other units,appropriate conversion can be applied to use those measurements in theabove interpretation function.

The DAI score can be further rounded and capped, in order to provide awhole number between 1 and 100, the scaled DAI score. To accomplishthis, the immediately preceding function can be re-written: scaled DAIscore=round(max(min((0.56*sqrt(IPTJC)+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(CRP+1)+0.96)*10.53+1,1001)). Biomarker gene names provided in the above formulas representthe concentrations of those markers, and will depend on the types ofassays used.

In some embodiments of the present teachings, it is not required thatthe DAI score be compared to any pre-determined “reference,” “normal,”“control,” “standard,” “healthy,” “pre-disease” or other like index, inorder for the DAI score to provide a quantitative measure ofinflammatory disease activity in the subject.

In other embodiments of the present teachings, the amount of thebiomarker(s) can be measured in a sample and used to derive a DAI score,which DAI score is then compared to a “normal” or “control” level orvalue, utilizing techniques such as, e.g., reference or discriminationlimits or risk defining thresholds, in order to define cut-off pointsand/or abnormal values for inflammatory disease. The normal level thenis the level of one or more biomarkers or combined biomarker indicestypically found in a subject who is not suffering from the inflammatorydisease under evaluation. Other terms for “normal” or “control” are,e.g., “reference,” “index,” “baseline,” “standard,” “healthy,”“pre-disease,” etc. Such normal levels can vary, based on whether abiomarker is used alone or in a formula combined with other biomarkersto output a score. Alternatively, the normal level can be a database ofbiomarker patterns from previously tested subjects who did not convertto the inflammatory disease under evaluation over a clinically relevanttime period. Reference (normal, control) values can also be derivedfrom, e.g., a control subject or population whose inflammatory diseaseactivity level or state is known. In some embodiments of the presentteachings, the reference value can be derived from one or more subjectswho have been exposed to treatment for inflammatory disease, or from oneor more subjects who are at low risk of developing inflammatory disease,or from subjects who have shown improvements in inflammatory diseaseactivity factors (such as, e.g., clinical parameters as defined herein)as a result of exposure to treatment. In some embodiments the referencevalue can be derived from one or more subjects who have not been exposedto treatment; for example, samples can be collected from (a) subjectswho have received initial treatment for inflammatory disease, and (b)subjects who have received subsequent treatment for inflammatorydisease, to monitor the progress of the treatment. A reference value canalso be derived from disease activity algorithms or computed indicesfrom population studies.

Systems for Implementing Disease Activity Tests

Tests for measuring disease activity according to various embodiments ofthe present teachings can be implemented on a variety of systemstypically used for obtaining test results, such as results fromimmunological or nucleic acid detection assays. Such systems maycomprise modules that automate sample preparation, that automate testingsuch as measuring biomarker levels, that facilitate testing of multiplesamples, and/or are programmed to assay the same test or different testson each sample. In some embodiments, the testing system comprises one ormore of a sample preparation module, a clinical chemistry module, and animmunoassay module on one platform. Testing systems are typicallydesigned such that they also comprise modules to collect, store, andtrack results, such as by connecting to and utilizing a databaseresiding on hardware. Examples of these modules include physical andelectronic data storage devices as are well-known in the art, such as ahard drive, flash memory, and magnetic tape. Test systems also generallycomprise a module for reporting and/or visualizing results. Someexamples of reporting modules include a visible display or graphicaluser interface, links to a database, a printer, etc. See sectionMachine-readable storage medium, below.

One embodiment of the present invention comprises a system fordetermining the inflammatory disease activity of a subject. In someembodiments, the system employs a module for applying a DAIMRK or ALLMRKformula to an input comprising the measured levels of biomarkers in apanel, as described herein, and outputting a disease activity indexscore. In some embodiments, the measured biomarker levels are testresults, which serve as inputs to a computer that is programmed to applythe DAIMRK or ALLMRK formula. The system may comprise other inputs inaddition to or in combination with biomarker results in order to derivean output disease activity index; e.g., one or more clinical parameterssuch as therapeutic regimen, TJC, SJC, morning stiffness, arthritis ofthree or more joint areas, arthritis of hand joints, symmetricarthritis, rheumatoid nodules, radiographic changes and other imaging,gender/sex, age, race/ethnicity, disease duration, height, weight,body-mass index, family history, CCP status, RF status, ESR,smoker/non-smoker, etc. In some embodiments the system can apply theDAIMRK/ALLMRK formula to biomarker level inputs, and then output adisease activity score that can then be analyzed in conjunction withother inputs such as other clinical parameters. In other embodiments,the system is designed to apply the DAIMRK/ALLMRK formula to thebiomarker and non-biomarker inputs (such as clinical parameters)together, and then report a composite output disease activity index.

A number of testing systems are presently available that could be usedto implement various embodiments of the present teachings. See, forexample, the ARCHITECT series of integrated immunochemistrysystems—high-throughput, automated, clinical chemistry analyzers(ARCHITECT is a registered trademark of Abbott Laboratories, AbbottPark, Ill. 60064). See C. Wilson et al., “Clinical Chemistry AnalyzerSub-System Level Performance,” American Association for ClinicalChemistry Annual Meeting, Chicago, Ill., Jul. 23-27, 2006; and, H JKisner, “Product development: the making of the Abbott ARCHITECT,” Clin.Lab. Manage. Rev. 1997 November-December, 11(6):419-21; A. Ognibene etal., “A new modular chemiluminescence immunoassay analyser evaluated,”Clin. Chem. Lab. Med. 2000 March, 38(3):251-60; J W Park et al.,“Three-year experience in using total laboratory automation system,”Southeast Asian J. Trop. Med. Public Health 2002, 33 Suppl 2:68-73; D.Pauli et al., “The Abbott Architect c8000: analytical performance andproductivity characteristics of a new analyzer applied to generalchemistry testing,” Clin. Lab. 2005, 51(1-2):31-41.

Another testing system useful for embodiments of the present teachingsis the VITROS system (VITROS is a registered trademark of Johnson &Johnson Corp., New Brunswick, N.J.)—an apparatus for chemistry analysisthat is used to generate test results from blood and other body fluidsfor laboratories and clinics. Another testing system is the DIMENSIONsystem (DIMENSION is a registered trademark of Dade Behring Inc.,Deerfield Ill.)—a system for the analysis of body fluids, comprisingcomputer software and hardware for operating the analyzers, andanalyzing the data generated by the analyzers.

The testing required for various embodiments of the present teachings,e.g. measuring biomarker levels, can be performed by laboratories suchas those certified under the Clinical Laboratory Improvement Amendments(42 U.S.C. Section 263(a)), or by laboratories certified under any otherfederal or state law, or the law of any other country, state or provincethat governs the operation of laboratories that analyze samples forclinical purposes. Such laboratories include, for example, LaboratoryCorporation of America, 358 South Main Street, Burlington, N.C. 27215(corporate headquarters); Quest Diagnostics, 3 Giralda Farms, Madison,N.J. 07940 (corporate headquarters); and other reference and clinicalchemistry laboratories.

Biomarker Selection

The biomarkers and methods of the present teachings allow one of skillin the art to monitor or assess a subject's inflammatory and/orautoimmune disease activity, such as for RA, with a high degree ofaccuracy. Over 100 markers were initially identified as having increasedor decreased concentration levels in subjects or populations with RArelative to subjects without disease, or at different states of disease,or to the subject himself at other timepoints in the evolution oractivity of the disease. For the initial comparison of observedbiomarker with RA disease activity, the disease activity for eachsubject was based upon traditional clinical parameters, such as theDAS28 score.

DAIMRK Group of Markers

Analyte biomarkers can be selected for use in the present teachings toform a panel or group of markers. Table 1 describes several specificbiomarkers, collectively referred to as the DAIMRK group of biomarkers.The present teachings describe the DAIMRK set of biomarkers as one setor panel of markers that is strongly associated with inflammatorydisease, and especially RA, when used in particular combinations toderive a DAI score, based on their correlation with traditional clinicalassessments of disease; in the example of RA, by their correlation withDAS28. See Example 1. As an example, one embodiment of the presentteachings comprises a method of determining RA disease activity in asubject comprising measuring the levels of at least two biomarkers fromTable 1, wherein the at least two biomarkers are selected from the groupconsisting of apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3);chemokine (C-C motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilageglycoprotein-39) (CHI3L1); ICTP; C-reactive protein, pentraxin-related(CRP); epidermal growth factor (beta-urogastrone) (EGF); intercellularadhesion molecule 1 (ICAM1); interleukin 18 (interferon-gamma-inducingfactor) (IL18); interleukin 1, beta (IL1B); interleukin 1 receptorantagonist (IL1RN); interleukin 6 (interferon, beta 2) (IL6);interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan sulfate;leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase)(MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3);resistin (RETN); calprotectin (heteropolymer of protein subunits S100ASand S100A9); serum amyloid A1 (SAA1); tumor necrosis factor receptorsuperfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1(VCAM1); vascular endothelial growth factor A (VEGPFA); and,pyridinoline (PYD); then, using these observed biomarker levels toderive a disease activity index score for the subject via aninterpretation function, which score provides a quantitative measure ofRA disease activity in that subject.

One skilled in the art will recognize that the DAIMRK biomarkerspresented herein encompass all forms and variants of these biomarkers,including but not limited to polymorphisms, isoforms, mutants,derivatives, transcript variants, precursors (including nucleic acidsand pre- or pro-proteins), cleavage products, receptors (includingsoluble and transmembrane receptors), ligands, protein-ligand complexes,protein-protein homo- or heteropolymers, post-translationally modifiedvariants (such as, e.g., via cross-linking or glycosylation), fragments,and degradation products, as well as any multi-unit nucleic acid,protein, and glycoprotein structures comprising any of the DAIMRKbiomarkers as constituent subunits of the fully assembled structure.

TABLE 1 Entrez DAIMRK Official Official Other NCBI Gene No. Symbol*Name* Name(s) RefSeq ID  1 APOA1 Apolipoprotein MGC117399; NP_000030.1335 A-I ApoAI  2 APOC3 Apolipoprotein ApoCIII; NP_000031.1 345 C-IIIMGC150353  3 CCL22 Chemokine MDC; A- NP_002981.2 6367 (C-C motif)152E5.1; ABCD- ligand 22 1; DC/B-CK; MGC34554; SCYA22; STCP- 1; CCchemokine STCP-1; macrophage- derived chemokine; small induciblecytokine A22; small inducible cytokine subfamily A (Cys-Cys), member 22;stimulated T cell chemotactic protein 1  4 CHI3L Chitinase 3-likeYKL-40; NP_001267.2 1116 1 (cartilage ASRT7; glycoprotein-39)DKFZp686N19119; FLJ38139; GP39; HC-gp39; HCGP-3P; YYL- 40; cartilageglycoprotein-39; chitinase 3-like 1  5 CRP C-reactive MGC149895;NP_000558.2 1401 protein, MGC88244; pentraxin-related PTX1  6 EGFEpidermal HOMG4; URG; NP_001954.2 1950 growth factor beta-urogastrone;(beta- epidermal growth urogastrone) factor  7 ICAMI Intercellularintercellular NP_000192.2 3383 adhesion adhesion molecule 1 molecule 1(CD54); human rhinovirus receptor; ICAM-1  8 N/A N/A ICTP N/A N/A  9IL18 Interleukin 18 IGIF; IL-1g; NP_001553.1 3606 (interferon- IL1F4;IL-18; gamma-inducing MGC12320; IL-1 factor) gamma; interferon-gamma-inducing factor; interleukin-1 gamma 10 IL1B Interleukin 1, IL-1;IL1-BETA; NP_000567.1 3553 Beta IL1β; IL1F2; catabolin; preinterleukin 1beta; pro- interleukin-1- beta 11 IL1RN Interleukin 1 DIRA; ICIL-NP_000568.1 3557 receptor 1RA; IL-1ra3; antagonist IL1F3; IL1RA; IRAP;MGC10430; MVCD4; IL1RN (IL1F3); OTTHUMP00000203730; intracellular IL- 1receptor antagonist type II; intracellular interleukin- 1 receptorantagonist (icIL- 1ra); type II interleukin-1 receptor antagonist 12 IL6Interleukin 6 IL-6; BSF2; NP_000591.1 3569 (interferon, HGF; HSF; beta2) IFNB2; B cell stimulatory factor-2; B-cell differentiation factor;CTL differentiation factor; OTTHUMP00000158544; hybridoma growth factor;interleukin BSF-2 13 IL6R Interleukin 6 IL-6R; CD126; NP_000556.1 3570receptor IL-6R-alpha; IL6RA; MGC104991; CD126 antigen; interleukin 6receptor alpha subunit 14 IL8 Interleukin 8 IL-8; CXCL8; NP_000575.13576 GCP1; LECT; LUCT; LYNAP; MDNCF; MONAP; NAF; NAP-1; T cellchemotactic factor; beta- thromboglobulm- like protein; chemokine (C-X-C motif) ligand 8; emoctakin; granulocyte chemotactic protein 1;lymphocyte- derived neutrophil- activating factor; monocyte- derivedneutrophil chemotactic factor; neutrophil- activating peptide 1; smallinducible cytokine subfamily B, member 8 15^(†) N/A N/A keratan sulfate;N/A N/A KS 16 LEP Leptin FLJ94114; OB; NP_000221.1 3952 OBS; leptin(murine obesity homolog); leptin (obesity homolog, mouse); obese, mouse,homolog of; obesity factor 17 MMP1 Matrix MMP-1; CLG; NP_002412.1 4312metallopeptidase CLGN; fibroblast 1 (interstitial collagenase;collagenase) matrix metalloprotease 1 18 MMP3 Matrix MMP-3; NP_002413.14314 metallopeptidase CHDS6; 3 (stromelysin MGC126102; 1, progelatinase)MGC126103; MGC126104; SL-1; STMY; STMY1; STR1; proteoglycanase;transin-1 19 RETN Resistin ADSF; FIZZ3; NP_065148.1 56729 MGC126603;MGC126609; RETN1; RSTN; XCP1; C/EBP- epsilon regulated myeloid-specificsecreted cysteine-rich protein precursor 1; found in inflammatory zone 320^(‡) S100A8 S100 calcium Calprotectin; NP_002955.2 6279 bindingprotein 60B8AG; A8 CAGA; CFAG; CGLA; CP-10; L1Ag; MA387; MIF; MRP8; NIF;P8; myeloid related protein 8; OTTHUMP00000015330; S100 calcium-bindingprotein A8; S100 calcium-binding protein A8 (calgranulin A); calgranulinA; cystic fibrosis antigen S100A9 S100 calcium Calprotectin; NP_002956.16280 binding protein 60B8AG; A9 CAGB; CFAG; CGLB; L1AG; L1AG; MAC387;MIF; MRP14; NIF; P14; myeloid related protein 9; S100 calcium-bindingprotein A9; S100 calcium-binding protein A9 (calgranulin B); calgranulinB 21 SAA1 Serum amyloid MGC111216; NP_000322.2 6288 A1 PIG4; SAA;TP53I4; tumor protein p53 inducible protein 4 22 TNFRSF1A Tumor necrosisTNFR1; NP_001056.1 7132 factor receptor CD120a; FPF; superfamily,MGC19588; member 1A TBP1; TNF-R; TNF-R55; TNFAR; TNFR55; TNFR60; p55;p55-R; p60; tumor necrosis factor binding protein 1; tumor necrosisfactor receptor 1; tumor necrosis factor receptor type 1; tumor necrosisfactor-alpha receptor 23 TNFSF13B Tumor necrosis BAFF; BLYS;NP_001139117.1; 10673 factor (ligand) CD257; DTL; NP_006564.1superfamily, TALL1; member 13b THANK; TNFSF20; ZTNF4; B cell activationfactor 24 VCAM1 Vascular cell VCAM-1; NP_001069.1 7412 adhesion CID106;molecule 1 DKFZp779G2333; INCAM-100; MGC99561; CD106 antigen 25 VEGFAVascular RP1-261G23.1; NP_001020539.2 7422 endothelial MGC70609; growthfactor MVCD1; VEGF; A VPF; vascular endothelial growth factor isoformVEGF165; vascular permeability factor 26 N/A N/A PYD, N/A N/Apyridinoline *HUGO Gene Nomenclature Committee, as of Sep. 25, 2009;accession numbers refer to sequence versions in NCBI database as of Sep.25, 2009. ^(†)Keratan sulfate; not a discrete gene ^(‡)Calprotectinheteropolymer N/A = Not applicable to this analyte

Biological Significance of the DAIMRK Group of Markers

The present teachings describe a robust, stepwise development processfor identifying a panel or panels of biomarkers that are stronglypredictive of autoimmune disease activity. Multivariate algorithmiccombinations of specific biomarkers as described herein exceed theprognostic and predictive power of individual biomarkers known in theart, because the combinations comprise biomarkers that represent a broadrange of disease mechanisms, which no individual biomarker does. As aconsequence of the diversity of pathways represented by the combinationsas taught herein, the methods of the present teachings are useful in theclinical assessment of individual subjects, despite the heterogeneity ofthe pathology of the disease assessed.

The group of biomarkers comprising the DAIMRK set, as an example, wasidentified through a selection process comprising rigorous correlationstudies of an initial large, comprehensive set of candidate proteinbiomarkers, the ALLMRK set (also described herein). See, e.g.,Example 1. All of the biomarkers that resulted from these correlationstudies, and that make up the DAIMRK set, are known in the art to playkey roles in the pathology of the autoimmune disease, RA. Themethodology employed in selecting the DAIMRK biomarkers thus resulted ina set of markers especially useful in quantifying RA disease activity,by providing the clinician with a unique and broad look at RA diseasebiology. The DAIMRK set of biomarkers of the present teachings are thusmore effective in quantifying disease activity than single biomarkers orrandomly selected groupings of biomarkers.

By demonstration of the key roles of the resulting DAIMRK markers in RApathology, the DAIMRK set comprises: the endogenous form of therecombinant molecule anakinra, an FDA-approved biologic therapy for RA(IL1RN); the target of anakinra, IL1B, an inflammatory mediator and keypathologic regulator in RA; key mediators of the IL6 pathway (IL6 andIL6R) and the TNF pathway (TNFRSF1A), which are also targets of biologictherapies in RA; IL8, which modulates neutrophil migration andactivation, neutrophils having a key role in RA disease, as theycomprise the majority of infiltrating inflammatory cells in RA synovialfluid and release a variety of disease mediators; calprotectin, whichhas a role in modulating neutrophil activation, in addition to its rolein TLR4 inflammatory signaling; CCL22, a key modulator of humoralimmunity and B cell activation, and which recruits T cells to therheumatoid synovium; the pro-angiogenic proteins VEGFA and IL8, whichalso attract leukocytes to the RA joint; the endothelial adhesion andactivation biomarkers ICAM1 and VCAM1; markers derived in large partfrom fibroblasts, including IL6, IL8, VEGPFA, EGF, MMP1 and MMP3;CHI3L1, which is highly elevated in RA joints and thought to modulateintra-articular matrix; bone and cartilage matrix breakdown products ofRA joints, including ICTP, keratan sulfate, and PYD; lipid-associatedproteins LEP, RETN, APOA1 and APOC3; and, two key acute phase proteins,CRP and SAA1, which reflect the role of RA inflammation in inducing thehepatic acute phase response.

Additionally, because the serum levels of certain protein biomarkers ofthe DAIMRK set are known to fluctuate in an individual, depending ondisease activity, in some embodiments of the present teachings theclinician could select those biomarkers for generating a DAI score, andthus obtain a more concise overview of the subject's present diseaseactivity status.

Moreover, the process of comprehensive candidate biomarkeridentification and subsequent staged correlation-based analyses in aseries of independent cohorts, as described in the Examples that follow,results in the identification of a panel or panels of biomarkers thathave significant correlation to disease activity.

Model Development Process

An exemplary method for developing predictive models to determine theinflammatory disease activity of a subject or population is shown by theflow diagram of FIG. 6 (200). Biomarker data from a representativepopulation, as described herein, is obtained (202). This biomarker datacan be derived through a variety of methods, including prospective,retrospective, cross-sectional, or longitudinal studies, that involveinterventions or observations of the representative subjects orpopulations from one or more timepoints. The biomarker data can beobtained from a single study or multiple studies. Subject and populationdata can generally include data pertaining to the subjects' diseasestatus and/or clinical assessments, which can be used for training andvalidating the algorithms for use in the present teachings, wherein thevalues of the biomarkers described herein are correlated to the desiredclinical measurements.

Data within the representative population dataset is then prepared (204)so as to fit the requirements of the model that will be used forbiomarker selection, described below. A variety of methods of datapreparation can be used, such as transformations, normalizations, andgap-fill techniques including nearest neighbor interpolation or otherpattern recognition techniques. The data preparation techniques that areuseful for different model types are well-known in the art. SeeExamples, below.

Biomarkers are then selected for use in the training of the model todetermine inflammatory disease activity (206). Various models can beused to inform this selection, and biomarker data are chosen from thedataset providing the most reproducible results. Methods to evaluatebiomarker performance can include, e.g., bootstrapping andcross-validation.

After the biomarkers are selected, the model to be used to determineinflammatory disease activity can be selected. For specific examples ofstatistical methods useful in designing predictive models, seeCalculation of the DAI score.

For the particular selection model used with a dataset, biomarkers canbe selected based on such criteria as the biomarker's ranking among allcandidate markers, the biomarker's statistical significance in themodel, and any improvement in model performance when the biomarker isadded to the model. Tests for statistical significance can include, forexample, correlation tests, t-tests, and analysis of variance (ANOVA).Models can include, for example, regression models such as regressiontrees and linear models, and classification models such as logisticregression, Random Forest, SVM, tree models, and LDA. Examples of theseare described herein.

In those cases where individual biomarkers are not alone indicative ofinflammatory disease activity, biomarker combinations can be applied tothe selection model. Instead of univariate biomarker selection, forexample, multivariate biomarker selection can be used. One example of analgorithm useful in multivariate biomarker selection is a recursivefeature selection algorithm. Biomarkers that are not alone goodindicators of inflammatory disease activity may still be useful asindicators when in combination with other biomarkers, in a multivariateinput to the model, because each biomarker may bring additionalinformation to the combination that would not be informative where takenalone.

Next, selection, training and validation is performed on the model forassessing disease activity (208). Models can be selected based onvarious performance and/or accuracy criteria, such as are describedherein. By applying datasets to different models, the results can beused to select the best models, while at the same time the models can beused to determine which biomarkers are statistically significant forinflammatory disease activity. Combinations of models and biomarkers canbe compared and validated in different datasets. The comparisons andvalidations can be repeated in order to train and/or choose a particularmodel.

FIG. 7 is a flow diagram of an exemplary method (250) of using a modelas developed above to determine the inflammatory disease activity of asubject or a population. Biomarker data is obtained from the subject at(252). This data can be obtained by a variety of means, including butnot limited to physical examinations, self-reports by the subject,laboratory testing, medical records and charts. Subject data can then beprepared (254) via transformations, logs, normalizations, and so forth,based on the particular model selected and trained in FIG. 6. The datais then input into the model for evaluation (256), which outputs anindex value (258); e.g., a DAI score. Examples as to how a model can beused to evaluate a subject's biomarkers and output a DAI value areprovided herein.

Modifications for Response to Treatment

In certain embodiments of the present teachings, biomarkers from theDAIMRK group can be used to determine a subject's response to treatmentfor inflammatory disease. Measuring levels of an effective amount ofbiomarkers also allows for the course of treatment of inflammatorydisease to be monitored. In these embodiments, a biological sample canbe provided from a subject undergoing therapeutic regimens forinflammatory disease. If desired, biological samples are obtained fromthe subject at various time points before, during, or after treatment.

Various embodiments of the present teachings can be used to provide aguide to the selection of a therapeutic regimen for a subject; meaning,e.g., that treatment may need to be more or less aggressive, or asubject may need a different therapeutic regimen, or the subject'scurrent therapeutic regimen may need to be changed or stopped, or a newtherapeutic regimen may need to be adopted, etc.

Treatment strategies are confounded by the fact that RA is aclassification given to a group of subjects with a diverse array ofrelated symptoms. This suggests that certain subtypes of RA are drivenby specific cell type or cytokine. As a likely consequence, no singletherapy has proven optimal for treatment. Given the increasing numbersof therapeutic options available for RA, the need for an individuallytailored treatment directed by immunological prognostic factors oftreatment outcome is imperative. In various embodiments of the presentteachings, a DAIMRK biomarker-derived algorithm can be used to quantifytherapy response in RA subjects. See Example 5. Measuring DAIMRKbiomarker levels over a period time can provide the clinician with adynamic picture of the subject's biological state, and the DAI scoresare highly correlated to DAS28. Overlaying the DAS28 score with the DAIscore can provide a deeper understanding of how a subject is respondingto therapy. These embodiments of the present teachings thus will providesubject-specific biological information, which will be informative fortherapy decision and will facilitate therapy response monitoring, andshould result in more rapid and more optimized treatment, better controlof disease activity, and an increase in the proportion of subjectsachieving remission.

Differences in the genetic makeup of subjects can result in differencesin their relative abilities to metabolize various drugs, which maymodulate the symptoms or state of inflammatory disease. Subjects thathave inflammatory disease can vary in age, ethnicity, body mass index(BMI), total cholesterol levels, blood glucose levels, blood pressure,LDL and HDL levels, and other parameters. Accordingly, use of thebiomarkers disclosed herein, both alone and together in combination withknown genetic factors for drug metabolism, allow for a pre-determinedlevel of predictability that a putative therapeutic or prophylactic tobe tested in a selected subject will be suitable for treating orpreventing inflammatory disease in the subject.

To identify therapeutics or drugs that are appropriate for a specificsubject, a test sample from the subject can also be exposed to atherapeutic agent or a drug, and the level of one or more biomarkers canbe determined. The level of one or more biomarkers can be compared tosample derived from the subject before and after treatment or exposureto a therapeutic agent or a drug, or can be compared to samples derivedfrom one or more subjects who have shown improvements in inflammatorydisease state or activity (e.g., clinical parameters or traditionallaboratory risk factors) as a result of such treatment or exposure.

Combination with Clinical Parameters

Any of the aforementioned clinical parameters can also be used in thepractice of the present teachings, as input to the DAIMRK formula or asa pre-selection criteria defining a relevant population to be measuredusing a particular DAIMRK panel and formula. As noted above, clinicalparameters can also be useful in the biomarker normalization andpre-processing, or in selecting particular biomarkers from DAIMRK, panelconstruction, formula type selection and derivation, and formula resultpost-processing.

Clinical Assessments of the Present Teachings

In some embodiments of the present teachings, panels of DAIMRKbiomarkers and formulas are tailored to the population, endpoints orclinical assessment, and/or use that is intended. For example, theDAIMRK panels and formulas can be used to assess subjects for primaryprevention and diagnosis, and for secondary prevention and management.For the primary assessment, the DAIMRK panels and formulas can be usedfor prediction and risk stratification for future conditions or diseasesequelae, for the diagnosis of inflammatory disease, for the prognosisof disease activity and rate of change, and for indications for futurediagnosis and therapeutic regimens. For secondary prevention andclinical management, the DAIMRK panels and formulas can be used forprognosis and risk stratification. The DAIMRK panels and formulas can beused for clinical decision support, such as determining whether to deferintervention or treatment, to recommend preventive check-ups for at-riskpatients, to recommend increased visit frequency, to recommend increasedtesting, and to recommend intervention. The DAIMRK panels and formulascan also be useful for therapeutic selection, determining response totreatment, adjustment and dosing of treatment, monitoring ongoingtherapeutic efficiency, and indication for change in therapeuticregimen.

In some embodiments of the present teachings, the DAIMRK panels andformulas can be used to aid in the diagnosis of inflammatory disease,and in the determination of the severity of inflammatory disease. TheDAIMRK panels and formulas can also be used for determining the futurestatus of intervention such as, for example in RA, determining theprognosis of future joint erosion with or without treatment. Certainembodiments of the present teachings can be tailored to a specifictreatment or a combination of treatments. X-ray is currently consideredthe gold standard for assessment of disease progression, but it haslimited capabilities since subjects may have long periods of activesymptomatic disease while radiographs remain normal or show onlynonspecific changes. Conversely, subjects who seem to have quiescentdisease (subclinical disease) may slowly progress over time, undetectedclinically until significant radiographic progression has occurred. Ifsubjects with a high likelihood of disease progression could beidentified in advance, the opportunity for early aggressive treatmentcould result in much more effective disease outcomes. See, e.g., M.Weinblatt et al., N. Engl. J. Med. 1999, 340:253-259. In certainembodiments of the present teachings, an algorithm developed from theDAIMRK set of biomarkers can be used, with significant power, tocharacterize the level of bone or cartilage damage activity in RAsubjects. See Example 6. In other embodiments, an algorithm developedfrom the DAIMRK set of biomarkers can be used, with significant power,to prognose joint destruction over time. See Example 6. In otherembodiments, the DAI score can be used as a strong predictor ofradiographic progression, giving the clinician a novel way to identifysubjects at risk of RA-induced joint damage and allowing for earlyprescription of joint-sparing agents, prophylactically.

In some embodiments of the present teachings, the DAIMRK panels andformulas can be used as surrogate markers of clinical events necessaryfor the development of inflammatory disease-specific agents; e.g.,pharmaceutical agents. That is, the DAI surrogate marker, derived from aDAIMRK panel, can be used in the place of clinical events in a clinicaltrial for an experimental RA treatment. DAIMRK panels and formulas canthus be used to derive an inflammatory disease surrogate endpoint toassist in the design of experimental treatments for RA.

Measurement of Biomarker

The quantity of one or more biomarkers of the present teachings can beindicated as a value. The value can be one or more numerical valuesresulting from the evaluation of a sample, and can be derived, e.g., bymeasuring level(s) of the biomarker(s) in a sample by an assay performedin a laboratory, or from dataset obtained from a provider such as alaboratory, or from a dataset stored on a server. Biomarker levels canbe measured using any of several techniques known in the art. Thepresent teachings encompass such techniques, and further include allsubject fasting and/or temporal-based sampling procedures for measuringbiomarkers.

The actual measurement of levels of the biomarkers can be determined atthe protein or nucleic acid level using any method known in the art.“Protein” detection comprises detection of full-length proteins, matureproteins, pre-proteins, polypeptides, isoforms, mutations, variants,post-translationally modified proteins and variants thereof, and can bedetected in any suitable manner. Levels of biomarkers can be determinedat the protein level, e.g., by measuring the serum levels of peptidesencoded by the gene products described herein, or by measuring theenzymatic activities of these protein biomarkers. Such methods arewell-known in the art and include, e.g., immunoassays based onantibodies to proteins encoded by the genes, aptamers or molecularimprints. Any biological material can be used for thedetection/quantification of the protein or its activity. Alternatively,a suitable method can be selected to determine the activity of proteinsencoded by the biomarker genes according to the activity of each proteinanalyzed. For biomarker proteins, polypeptides, isoforms, mutations, andvariants thereof known to have enzymatic activity, the activities can bedetermined in vitro using enzyme assays known in the art. Such assaysinclude, without limitation, protease assays, kinase assays, phosphataseassays, reductase assays, among many others. Modulation of the kineticsof enzyme activities can be determined by measuring the rate constant KMusing known algorithms, such as the Hill plot, Michaelis-Mentenequation, linear regression plots such as Lineweaver-Burk analysis, andScatchard plot.

Using sequence information provided by the public database entries forthe biomarker, expression of the biomarker can be detected and measuredusing techniques well-known to those of skill in the art. For example,nucleic acid sequences in the sequence databases that correspond tonucleic acids of biomarkers can be used to construct primers and probesfor detecting and/or measuring biomarker nucleic acids. These probes canbe used in, e.g., Northern or Southern blot hybridization analyses,ribonuclease protection assays, and/or methods that quantitativelyamplify specific nucleic acid sequences. As another example, sequencesfrom sequence databases can be used to construct primers forspecifically amplifying biomarker sequences in, e.g.,amplification-based detection and quantitation methods such asreverse-transcription based polymerase chain reaction (RT-PCR) and PCR.When alterations in gene expression are associated with geneamplification, nucleotide deletions, polymorphisms, post-translationalmodifications and/or mutations, sequence comparisons in test andreference populations can be made by comparing relative amounts of theexamined DNA sequences in the test and reference populations.

As an example, Northern hybridization analysis using probes whichspecifically recognize one or more of these sequences can be used todetermine gene expression. Alternatively, expression can be measuredusing RT-PCR; e.g., polynucleotide primers specific for thedifferentially expressed biomarker mRNA sequences reverse-transcribe themRNA into DNA, which is then amplified in PCR and can be visualized andquantified. Biomarker RNA can also be quantified using, for example,other target amplification methods, such as TMA, SDA, and NASBA, orsignal amplification methods (e.g., bDNA), and the like. Ribonucleaseprotection assays can also be used, using probes that specificallyrecognize one or more biomarker mRNA sequences, to determine geneexpression.

Alternatively, biomarker protein and nucleic acid metabolites can bemeasured. The term “metabolite” includes any chemical or biochemicalproduct of a metabolic process, such as any compound produced by theprocessing, cleavage or consumption of a biological molecule (e.g., aprotein, nucleic acid, carbohydrate, or lipid). Metabolites can bedetected in a variety of ways known to one of skill in the art,including the refractive index spectroscopy (RI), ultra-violetspectroscopy (UV), fluorescence analysis, radiochemical analysis,near-infrared spectroscopy (near-IR), nuclear magnetic resonancespectroscopy (NMR), light scattering analysis (LS), mass spectrometry,pyrolysis mass spectrometry, nephelometry, dispersive Ramanspectroscopy, gas chromatography combined with mass spectrometry, liquidchromatography combined with mass spectrometry, matrix-assisted laserdesorption ionization-time of flight (MALDI-TOF) combined with massspectrometry, ion spray spectroscopy combined with mass spectrometry,capillary electrophoresis, NMR and IR detection. See WO 04/056456 and WO04/088309, each of which is hereby incorporated by reference in itsentirety. In this regard, other biomarker analytes can be measured usingthe above-mentioned detection methods, or other methods known to theskilled artisan. For example, circulating calcium ions (Ca²⁺) can bedetected in a sample using fluorescent dyes such as the Fluo series,Fura-2A, Rhod-2, among others. Other biomarker metabolites can besimilarly detected using reagents that are specifically designed ortailored to detect such metabolites.

In some embodiments, a biomarker is detected by contacting a subjectsample with reagents, generating complexes of reagent and analyte, anddetecting the complexes. Examples of “reagents” include but are notlimited to nucleic acid primers and antibodies.

In some embodiments of the present teachings an antibody binding assayis used to detect a biomarker, e.g., a sample from the subject iscontacted with an antibody reagent that binds the biomarker analyte, areaction product (or complex) comprising the antibody reagent andanalyte is generated, and the presence (or absence) or amount of thecomplex is determined. The antibody reagent useful in detectingbiomarker analytes can be monoclonal, polyclonal, chimeric, recombinant,or a fragment of the foregoing, as discussed in detail above, and thestep of detecting the reaction product can be carried out with anysuitable immunoassay. The sample from the subject is typically abiological fluid as described above, and can be the same sample ofbiological fluid as is used to conduct the method described above.

Immunoassays carried out in accordance with the present teachings can behomogeneous assays or heterogeneous assays. In a homogeneous assay theimmunological reaction can involve the specific antibody (e.g.,anti-biomarker protein antibody), a labeled analyte, and the sample ofinterest. The label produces a signal, and the signal arising from thelabel becomes modified, directly or indirectly, upon binding of thelabeled analyte to the antibody. Both the immunological reaction ofbinding, and detection of the extent of binding, can be carried out in ahomogeneous solution. Immunochemical labels which can be employedinclude but are not limited to free radicals, radioisotopes, fluorescentdyes, enzymes, bacteriophages, and coenzymes. Immunoassays includecompetition assays.

In a heterogeneous assay approach, the reagents can be the sample ofinterest, an antibody, and a reagent for producing a detectable signal.Samples as described above can be used. The antibody can be immobilizedon a support, such as a bead (such as protein A and protein G agarosebeads), plate or slide, and contacted with the sample suspected ofcontaining the biomarker in liquid phase. The support is separated fromthe liquid phase, and either the support phase or the liquid phase isexamined using methods known in the art for detecting signal. The signalis related to the presence of the analyte in the sample. Methods forproducing a detectable signal include but are not limited to the use ofradioactive labels, fluorescent labels, or enzyme labels. For example,if the antigen to be detected contains a second binding site, anantibody which binds to that site can be conjugated to a detectable(signal-generating) group and added to the liquid phase reactionsolution before the separation step. The presence of the detectablegroup on the solid support indicates the presence of the biomarker inthe test sample. Examples of suitable immunoassays include but are notlimited to oligonucleotides, immunoblotting, immunoprecipitation,immunofluorescence methods, chemiluminescence methods,electrochemiluminescence (ECL), and/or enzyme-linked immunoassays(ELISA).

Those skilled in the art will be familiar with numerous specificimmunoassay formats and variations thereof which can be useful forcarrying out the method disclosed herein. See, e.g., E. Maggio,Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, Fla. See alsoU.S. Pat. No. 4,727,022 to C. Skold et al., titled “Novel Methods forModulating Ligand-Receptor Interactions and their Application”; U.S.Pat. No. 4,659,678 to CC Forrest et al., titled “Immunoassay ofAntigens”; U.S. Pat. No. 4,376,110 to GS David et al., tided“Immunometric Assays Using Monoclonal Antibodies”; U.S. Pat. No.4,275,149 to D. Litman et al., titled “Macromolecular EnvironmentControl in Specific Receptor Assays”; U.S. Pat. No. 4,233,402 to E.Maggio et al., titled “Reagents and Method Employing Channeling”; and,U.S. Pat. No. 4,230,797 to R. Boguslaski et al., titled “HeterogenousSpecific Binding Assay Employing a Coenzyme as Label.”

Antibodies can be conjugated to a solid support suitable for adiagnostic assay (e.g., beads such as protein A or protein G agarose,microspheres, plates, slides or wells formed from materials such aslatex or polystyrene) in accordance with known techniques, such aspassive binding. Antibodies as described herein can likewise beconjugated to detectable labels or groups such as radiolabels (e.g.,35S, 125I, 131I), enzyme labels (e.g., horseradish peroxidase, alkalinephosphatase), and fluorescent labels (e.g., fluorescein, Alexa, greenfluorescent protein, rhodamine) in accordance with known techniques.

Antibodies may also be useful for detecting post-translationalmodifications of biomarkers. Examples of post-translationalmodifications include, but are not limited to tyrosine phosphorylation,threonine phosphorylation, serine phosphorylation, citrullination andglycosylation (e.g., O-GlcNAc). Such antibodies specifically detect thephosphorylated amino acids in a protein or proteins of interest, and canbe used in the immunoblotting, immunofluorescence, and ELISA assaysdescribed herein. These antibodies are well-known to those skilled inthe art, and commercially available. Post-translational modificationscan also be determined using metastable ions in reflectormatrix-assisted laser desorption ionization-time of flight massspectrometry (MALDI-TOF). See U. Wirth et al., Proteomics 2002,2(10):1445-1451.

Kits

Other embodiments of the present teachings comprise biomarker detectionreagents packaged together in the form of a kit for conducting any ofthe assays of the present teachings. In certain embodiments, the kitscomprise oligonucleotides that specifically identify one or morebiomarker nucleic acids based on homology and/or complementarity withbiomarker nucleic acids. The oligonucleotide sequences may correspond tofragments of the biomarker nucleic acids. For example, theoligonucleotides can be more than 200, 200, 150, 100, 50, 25, 10, orfewer than 10 nucleotides in length. In other embodiments, the kitscomprise antibodies to proteins encoded by the biomarker nucleic acids.The kits of the present teachings can also comprise aptamers. The kitcan contain in separate containers a nucleic acid or antibody (theantibody either bound to a solid matrix, or packaged separately withreagents for binding to a matrix), control formulations (positive and/ornegative), and/or a detectable label, such as but not limited tofluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexadyes, luciferase, and radiolabels, among others. Instructions forcarrying out the assay, including, optionally, instructions forgenerating a DAI score, can be included in the kit; e.g., written, tape,VCR, or CD-ROM. The assay can for example be in the form of a Northernhybridization or a sandwich ELISA as known in the art.

In some embodiments of the present teachings, biomarker detectionreagents can be immobilized on a solid matrix, such as a porous strip,to form at least one biomarker detection site. In some embodiments, themeasurement or detection region of the porous strip can include aplurality of sites containing a nucleic acid. In some embodiments, thetest strip can also contain sites for negative and/or positive controls.Alternatively, control sites can be located on a separate strip from thetest strip. Optionally, the different detection sites can containdifferent amounts of immobilized nucleic acids, e.g., a higher amount inthe first detection site and lesser amounts in subsequent sites. Uponthe addition of test sample, the number of sites displaying a detectablesignal provides a quantitative indication of the amount of biomarkerpresent in the sample. The detection sites can be configured in anysuitably detectable shape and can be, e.g., in the shape of a bar or dotspanning the width of a test strip.

In other embodiments of the present teachings, the kit can contain anucleic acid substrate array comprising one or more nucleic acidsequences. The nucleic acids on the array specifically identify one ormore nucleic acid sequences represented by DAIMRK biomarker Nos. 1-25.In various embodiments, the expression of one or more of the sequencesrepresented by DAIMRK Nos. 1-25 can be identified by virtue of bindingto the array. In some embodiments the substrate array can be on a solidsubstrate, such as what is known as a “chip.” See, e.g., U.S. Pat. No.5,744,305. In some embodiments the substrate array can be a solutionarray; e.g., xMAP (Luminex, Austin, Tex.), Cyvera (Illumina, San Diego,Calif.), RayBio Antibody Arrays (RayBiotech, Inc., Norcross, Ga.),CellCard (Vitra Bioscience, Mountain View, Calif.) and Quantum Dots'Mosaic (Invitrogen, Carlsbad, Calif.).

Machine-Readable Storage Medium

A machine-readable storage medium can comprise, for example, a datastorage material that is encoded with machine-readable data or dataarrays. The data and machine-readable storage medium are capable ofbeing used for a variety of purposes, when using a machine programmedwith instructions for using said data. Such purposes include, withoutlimitation, storing, accessing and manipulating information relating tothe inflammatory disease activity of a subject or population over time,or disease activity in response to inflammatory disease treatment, orfor drug discovery for inflammatory disease, etc. Data comprisingmeasurements of the biomarkers of the present teachings, and/or theevaluation of disease activity or disease state from these biomarkers,can be implemented in computer programs that are executing onprogrammable computers, which comprise a processor, a data storagesystem, one or more input devices, one or more output devices, etc.Program code can be applied to the input data to perform the functionsdescribed herein, and to generate output information. This outputinformation can then be applied to one or more output devices, accordingto methods well-known in the art. The computer can be, for example, apersonal computer, a microcomputer, or a workstation of conventionaldesign.

The computer programs can be implemented in a high-level procedural orobject-oriented programming language, to communicate with a computersystem such as for example, the computer system illustrated in FIG. 16.The programs can also be implemented in machine or assembly language.The programming language can also be a compiled or interpreted language.Each computer program can be stored on storage media or a device such asROM, magnetic diskette, etc., and can be readable by a programmablecomputer for configuring and operating the computer when the storagemedia or device is read by the computer to perform the describedprocedures. Any health-related data management systems of the presentteachings can be considered to be implemented as a computer-readablestorage medium, configured with a computer program, where the storagemedium causes a computer to operate in a specific manner to performvarious functions, as described herein.

The biomarkers disclosed herein can be used to generate a “subjectbiomarker profile” taken from subjects who have inflammatory disease.The subject biomarker profiles can then be compared to a referencebiomarker profile, in order to diagnose or identify subjects withinflammatory disease, to monitor the progression or rate of progressionof inflammatory disease, or to monitor the effectiveness of treatmentfor inflammatory disease. The biomarker profiles, reference and subject,of embodiments of the present teachings can be contained in amachine-readable medium, such as analog tapes like those readable by aCD-ROM or USB flash media, among others. Such machine-readable media canalso contain additional test results, such as measurements of clinicalparameters and clinical assessments. The machine-readable media can alsocomprise subject information; e.g., the subject's medical or familyhistory. The machine-readable media can also contain informationrelating to other disease activity algorithms and computed scores orindices, such as those described herein.

EXAMPLES

Aspects of the present teachings can be further understood in light ofthe following examples, which should not be construed as limiting thescope of the present teachings in any way.

The practice of the present teachings employ, unless otherwiseindicated, conventional methods of protein chemistry, biochemistry,recombinant DNA techniques and pharmacology, within the skill of theart. Such techniques are explained fully in the literature. See, e.g.,T. Creighton, Proteins: Structures and Molecular Properties, 1993, W.Freeman and Co.; A. Lehninger, Biochemistry, Worth Publishers, Inc.(current addition); J. Sambrook et al., Molecular Cloning: A LaboratoryManual, 2nd Edition, 1989; Methods In Enzymology, S. Colowick and N.Kaplan, eds., Academic Press, Inc.; Remington's Pharmaceutical Sciences,18th Edition, 1990, Mack Publishing Company, Easton, Pa.; Carey andSundberg, Advanced Organic Chemistry, Vols. A and B, 3rd Edition, 1992,Plenum Press.

The practice of the present teachings also employ, unless otherwiseindicated, conventional methods of statistical analysis, within theskill of the art. Such techniques are explained fully in the literature.See, e.g., J. Little and D. Rubin. Statistical Analysis with MissingData, 2nd Edition 2002, John Wiley and Sons. Inc., NJ; M. Pepe, TheStatistical Evaluation of Medical Tests for Classification andPrediction (Oxford Statistical Science Series) 2003, Oxford UniversityPress, Oxford, UK; X. Zhoue et al., Statistical Methods in DiagnosticMedicine 2002, John Wiley and Sons, Inc., NJ; T. Hastie et. al, TheElements of Statistical Leaning: Data Mining, Inference, and Prediction,Second Edition 2009, Springer, N.Y.; W. Cooley and P. Lohnes,Multivariate procedures for the behavioral science 1962, John Wiley andSons, Inc. NY; E. Jackson, A User's Guide to Principal Components 2003,John Wiley and Sons, Inc., NY.

Example 1 Association of DAI with DAS28 Scores in a Large ClinicalCohort

Example 1 demonstrates the transformation of observed biomarker levelsinto a DAI score by various statistical modeling methodologies, whichDAI score serves as a quantitative measurement of disease activity thatcorrelates well with observed DAS28, as for measuring the extent ofsubject inflammation status and disease activity at any singletimepoint. Certain embodiments of the present teachings compriseutilizing the DAIMRK set of biomarkers for determining a DAI score withhigh correlation with disease activity status.

Samples were obtained from the Brigham and Women's Hospital RheumatoidArthritis Sequential Study (BRASS). The appropriate Research EthicsCommittee approval was obtained for the study, and all subjects gaveinformed consent. Since 2003, 1,000 subjects with confirmed RA under thecare of the Brigham and Women's hospital have been enrolled in BRASS.The cohort for this study had the following characteristics: 80% female,62% CCP positive, 83% RF positive, 13% smokers, 61% on MTX, 76% onnon-biologic DMARDs, 53% on biologic DMARDs, and 27% on steroids.Additionally, the mean age of the cohort was 59 years (standarddeviation (SD)+/−13.1), with a minimum age of 22 and a maximum age of94. The mean DAS28-CRP for this cohort was 4.1 (SD+/−1.7), with aminimum of 1.2 and a maximum of 8.2.

All subjects fulfilled the American College of Rheumatology criteria forRA, and every subject in the study will be followed for five years. Atsix-month intervals throughout the study, data are collected from allsubjects, comprising medical or clinical information such as diseaseactivity scores, radiological results, subject health status and otherclinical assessments. Blood samples are collected at twelve-monthintervals from each subject for five years. A subpopulation of onehundred and eighty subjects was selected from the BRASS cohort. Withinthe subjects selected, a wide distribution of DAS28-CRP scores wasrepresented (DAS28 range=1.19-8.2).

Assays were designed, in multiplex or ELISA format, for measuringmultiple disease-related protein biomarkers selected from the ALLMRKset, as that set is described herein. These assays were identifiedthrough a screening and optimization process, prior to assaying theBRASS samples. The respective biomarker assays, vendors, and platformsused were as follows: APOA1, Millipore, LUMINEX®; APOC3, Millipore,LUMINEX®; calprotectin, Alpco, ELISA; CCL22, Meso Scale Discovery, MSD®;CHL3L1 (YKL-40), Quidel, ELISA; CRP, Meso Scale Discovery, MSD®; EGF,R&D Systems, LUMINEX®; ICAM1, Meso Scale Discovery, MSD®; ICTP, IDS(Immunodiagnostic Systems), ELISA; IL18, R&D Systems, ELISA; IL1B, MesoScale Discovery, MSD®; IL1RN, R&D Systems, LUMINEX®; IL6. R&D Systems,LUMINEX®; IL6R, Millipore, LUMINEX®; IL8, R&D Systems, LUMINEX®; keratansulfate, Cape Cod, Inc., ELISA; LEP, R&D Systems, LUMINEX®; MMP1, R&DSystems, LUMINEX®; MMP3, R&D Systems, LUMINEX®; RETN, R&D Systems,LUMINEX®; SAA1, Meso Scale Discovery, MSD®; TNFRSF1A, Meso ScaleDelivery, MSD®; TNFSF13B, R&D Systems, ELISA; VCAM1, Meso ScaleDiscovery, MSD®; and, VEGFA, R&D Systems, LUMINEX®.

All assays were performed following the manufacturer's instructions,with cohort samples randomly assigned to the sample positions on theplate layouts. Four pooled sera, from healthy, RA, SLE andosteoarthritis (OA) subjects, were included in each assay plate asprocess controls. All samples were assayed at least in duplicate.Seven-point calibration curves were constructed for each biomarker foran accurate determination of the measurable range of test sera.

Prior to statistical analyses, all assay data were reviewed forpass/fail criteria as predefined by standard operating procedures,including inter-assay CV, intra-assay CV, percent number of sampleswithin the measurable range of the calibration curve, and four serumprocess controls within the range of the calibration curve. Thebiomarker values that were not in the measurable range of thecalibration curves were marked as missing data, and imputed by thelowest/highest detected value across all the samples within a givenbiomarker assay. No imputation was performed for the univariateanalyses. For multivariate analysis, missing data imputation methodscommonly used in microarray expression data and well-known in the artwere used. See, e.g., R. Little and D. Rubin, Statistical Analysis withMissing Data, 2nd Edition 2002, John Wiley and Sons, Inc., NJ.Biomarkers were excluded from analysis where more than 20% of the datawere missing, and the remaining data were imputed by the KNN algorithm(where k=5 nearest neighbors). KNN functions on the intuitive idea thatclose objects are more likely to be in the same category. Thus, in KNN,predictions are based on a set of prototype examples that are used topredict new (i.e., unseen) data based on the majority vote (forclassification tasks) over a set of k-nearest prototypes. Given a newcase of dependent values (query point), we would like to estimate theoutcome based on the KNN examples. KNN achieves this by finding kexamples that are closest in Euclidian distance to the query point.

Univariate Analysis

Biomarker assay data were normalized by plate before correlations werecalculated between individual proteins and measurements were transformedinto DAI scores. Associations were calculated between the DAI scores andDAS28-CRP scores, SJC, TJC, or CDAI. The correlation results were thencompared using univariate analysis. See Table 10.

TABLE 10 Biomarker Correlation coefficient Nominal p-value APOA1 −0.177<0.0001 calprotectin 0.42 <0.0001 CHI3LI 0.178 <0.0001 CRP 0.476 <0.0001EGF −0.358 <0.0001 ICAM1 0.242 <0.0001 IL1B −0.282 <0.0001 IL6 0.289<0.0001 IL6R 0.082 <0.0001 IL8 −0.393 <0.0001 IL1RN 0.211 <0.0001 LEP0.21 <0.0001 RETN 0.256 <0.0001 SAA1 0.386 <0.0001 TNFRSF1A 0.176<0.0001 VCAM1 0.323 <0.0001 VEGFA 0.198 <0.0001 keratan sulfate −0.2580.002 TNFSF13B 0.271 0.007 ICTP 0.266 0.014 APOC3 −0.118 0.255 MMP3 0.34<0.0001 CCL22 0.116 0.2 MMP1 0.261 0.006

See FIG. 8 for a cumulative distribution function (CDF) plot oftransformation comparisons, wherein the CDF of p-values is thecumulative distribution function of all the p-values obtained (i.e., onep-value per DAIMRK biomarker), and thus shows the distribution of allp-values. See FIG. 9 for a correlation matrix between 21 DAIMRKbiomarkers and continuous clinical variables.

The False Discovery Rate (FDR) was used as a multiple testingcorrection, according to the following: let k be the largest i for whichp_(i)≦i/m*α; reject all H_(i), where i=1, . . . , m. In this equationthe variable a is a pre-specified probability of a false-positive (TypeI) error, typically 0.05, and H is a hypothesis. As will be clear to oneof skill in the art, where the DAIMRK biomarker is significantlyassociated with the DAS score, the q-value (the false discovery rate) issmall. FIG. 8 shows the different results obtained from differentnormalizations. A parametric correlation test was also performed, usingthe parametric test H_(i): ρ_(i)=0, and the statistic given by

$t = {\frac{{r\left( {n - 2} \right)}^{1/2}}{\left( {1 - r^{2}} \right)^{1/2}}.}$

For this analysis, t represents the test statistics (for which p-valuecan be calculated using the T distribution), r is the correlationcoefficient, and n is the sample size.

Covariation and multicolinearity between all variables were evaluated;i.e., for both clinical data and biomarkers. Heatmap, PCA, andcorrelation matrices were generated. See FIGS. 11 and 9 for PCA andcorrelation matrices, respectively (heatmap not shown). If a strongcorrelation was shown to exist between biomarkers, it indicated thatmulticolinearity should be taken into account during the model buildingprocess. If a strong association was detected between baseline clinicalvariables and biomarkers, it was determined that further evaluation wasneeded. ANOVA and Spearman correlations, along with p-values and FDR,were used to examine associations between all clinical variables(without DAS28 scores) and biomarkers. See FIG. 9.

Multivariate Analysis

Several multivariate modeling methods were considered. In general, thelinear penalized regression methods were determined to perform the best.

Model 1: Forward Stepwise Ordinary Least Square Regression

For this modeling method, the equation Y=Xβ+ε applies, where Y is thecolumn vector with observed values, β is a matrix of coefficients forthe predictor variables X_(i), and ε is the random error. The forwardselection begins with no variables in the model. Then, given acollection of predictors X, the predictor having the largest absolutecorrelation with the response Y is selected and a simple linearregression of Y on X₁ is performed, where X₁ is the first predictorvariable. The residual vector is now orthogonal to X₁, and is taken tobe the new response variable. The other predictors are then projectedorthogonally to X₁ and the forward selection process is repeated. TheDAIMRK biomarker selected at each step is recorded, along with thecorrelation R².

Model 2: Penalized Regressions

Penalized regression model methods are a set of statistical techniquesthat select subsets of variables to include in a model and determinestable coefficients for the variables. These methods are particularlyuseful when variables are correlated, and include ridge regression,Lasso, Elastic Net, and other methods. All of these methods have thecharacteristic that they shrink (penalize) the coefficients in theregression model.

In the first penalized regression model, Least Absolute Shrinkage andSelection Operator (LASSO or Lasso) is used to prioritize biomarkers(based on R² values) and to obtain a Lasso model. The “lasso” in thismodel minimizes the residual sum of the square, subject to the sum ofthe absolute value of the coefficients being less than a constant. SeeR. Tibshirani, J. Royal Stat. Soc., series B 1996, 58(1):267-288. TheLasso method produces interpretable models, such as subset selection,and exhibits the stability of ridge regression (a statistical methodthat shrinks and stabilizes coefficients in regression models withmulticolinearity). See W. Mendenhall and T. Sincich, A Second Course inStatistics: Regression Analysis, 6^(th) edition 2003, Pearson PrenticeHall, Inc., Upper Saddle River, N.J.

In the second penalized regression model, linear regression is used withElastic Net and mixtures of Lasso and ridge penalties to prioritizebiomarkers (based on R² values) and obtain a final Elastic Net model.Elastic Net is a relatively new regularization and variable selectionmethod. It encourages a grouping effect, where strongly correlatedpredictors segregate together, tending to be either in or out of themodel together. See T. Zou, J. Royal Stat. Soc., series B 2005,67(2):301-320.

In the third model, the forward variable selection method is a method offinding the “best” combination of variables by starting with a singlevariable, that which results in the best fit for the dependent variableY, and increasing the number of variables used, step by step, testingall combinations of the original variable with the remaining variablesin order to find the “best” pair of variables, continuing until eitherall variables are used up or some stopping criterion is met.

Model 3: Random Forest

Random Forest models are based upon the idea of creating hundreds ofregression trees as models. See L Breiman, Machine Learning 2001,45(1):5-32. Each regression tree model is created with a uniform numberof terminal nodes (“leaves”) at the end of the branches of the tree. Toestimate the regression value of a new subject, or to assign the subjectto a class, the subject's data is evaluated within each of theregression tree models. The output prediction (i.e., regression value ifcontinuous data, classification if binary data) from all trees is thenaveraged to create the final regression value or class prediction. Inthe case of regression values, averaging may be obtained by a weightedaverage; in class prediction, simply by voting.

The Random Forest methodology is as follows. First, a bootstrap sample(i.e., a sample with replacement) is drawn from the original data. Thena regression tree is “grown” from each bootstrap sample; i.e., at eachnode one randomly samples p of the n biomarkers measured, and selectsthe best biomarker and the best value of that biomarker to split thedata into pure subsets from those biomarkers. Data from “training”subjects are used to build the tree models. Then, new data is predictedby aggregating the predictions of the various regression trees thusderived. For each subject sample k, where the k subject samples aredifferent from those used in training the model (i.e., all k samples are“out of the bag”), the response estimates are averaged over the trees,given as {circumflex over (γ)}_(k). The random forest predictionalgorithm is then given by the equation:

${{\hat{PE}}_{f} = {{E_{XY}\left( {Y - {\overset{\_}{h}(X)}} \right)}^{2} = {\frac{1}{K}{\sum\; \left( {y_{k} - \hat{y_{k}}} \right)^{2}}}}},$

where PÊ_(f) is a test set estimate of the generalization error ofPE_(f), and h(X)=(1/L)Σh(x;θ_(l)) is the random forest prediction. Thecollection of tree predictors is given by h(x,θ_(l)), l=1 . . . L, whereθ_(l) is a random vector. Y represents the actual response variables;e.g., a DAS score. Y represents the predictor, e.g., biomarker levels.

The variable importance is then estimated. In every regression tree thusgrown in the random forest, one calculates the prediction error for thattree,

${{PE}_{l} = {\frac{1}{K}{\sum\; \left( {y_{k} - \hat{y_{k}}} \right)^{2}}}},$

as predicted by the lth tree predictor, h(x;θ_(l)). One then randomlypermutes the values of a biomarker variable i in the “out of bag” cases,and computes the prediction error

${PE}_{li} = {\frac{1}{K}{\sum\; \left( {y_{k} - \hat{y_{ki}}} \right)^{2}}}$

as predicted by the lth tree predictor. Importance (Imp) is given as thevariable i for Imp_(i)=PE_(l)i−PE_(l) for the ith biomarker for lthtree. The variable importance of the ith variable is computed

$I_{i} = \frac{I\overset{\_}{m}p_{i}}{{SE}\left( {Imp}_{i} \right)}$

where Imp_(i) is the average and standard area of importance of ithvariable over all L trees.

Coefficients Representative of a DAI Model

The following coefficients represent the terms of the respective DAImodels: DAI_(k)=Σβ_(i)x_(ik), where DAI_(ik) is the calculated DAI forthe kth subject, x_(ik) represents the transformed ith biomarkerconcentration for the kth subject, and β_(i) is the coefficient for theith biomarker.

Cross-Validation

A random subset of 70% of the total study population was selectedwithout replacement. The model was fitted using this subset, thenevaluated as to AUC for classification of subjects, and correlation (r),against the remaining 30% of the study population. Cross-validation wasrepeated 100 times, and the resulting accuracy estimates were averagedto predict future performance.

Results

The analyses demonstrated that the DAI scores associate well with DAS28scores, and also discriminate between subjects with high and low DAS28scores. Correlations of the DAI scores with DAS28 were r=0.57 to r=0.6,as estimated using 100 test set cross-validations. Specifically, theDAS28 correlation of the DAI score derived using the Lasso method wasr=0.5909, the DAS28 correlation of the DAI score derived using theElastic Net method was r=0.5974, and the DAS28 correlation of the DAIscore derived using the forward variable selection method was r=0.5692.These results show that the DAI score derived from each of thesemethods, and using different subsets of the protein biomarkers, allyield good correlation with DAS28.

The DAI scores can also be used to discriminate between subjects withhigh and low DAS28 scores, and thus classify subjects by level ofdisease activity, as shown by the area under the ROC curve (FIGS. 12 and13), estimated using 100 cross-validation test sets. See also Example 3.Specifically, for subjects dichotomized at a DAS of 2.67, where DAS<2.67is considered remission, the area under the ROC curve for the DAI scorederived using the Lasso method was 0.911. The area under the ROC curvefor the DAI score derived using the Elastic Net method was 0.891. Forsubjects dichotomized on a DAS of 3.9, which is the median DAS value ofthis study, the area under the ROC curve for the DAI score derived usingthe Lasso method was 0.869. The area under the ROC curve for the DAIscore derived using the Elastic Net method was 0.856. These results showthat the DAI scores derived using each of these methods all yield goodareas under the ROC curves, and thus good discrimination betweensubjects with high and low DAS28 scores.

The results further show that by specifically selecting biomarkers fromthe DAIMRK set, all the DAI scores derived therefrom, according to eachof the above-described methods, yield good areas under the ROC curvesfor discriminating subjects with high and low DAS28 scores.

A specific instance of a formula for calculating a DAI score wasdeveloped using seven biomarker proteins selected from the DAIMRK set ofbiomarkers, according to the methods described above (starting with anALLMRK biomarker dataset, using data collected from 322 RA samplesobtained from the BRASS and OMRF cohorts; see below for a discussion ofthe OMRF cohort).

The DAI score in this Example was computed using the following formula:DAI=4.49+0.36*CRP−0.29*EGF−0.22*IL8+0.045*LEP+0.21*IL1RN−0.25*APOA1+0.10*CCL22.This formula exhibited a correlation of 0.5801 and AUC of 0.7772 inpredicting DAS28.

Example 2 Correlation of DAI to DAS28 Scores Over Multiple Timepoints ina Longitudinal Cohort

Example 2 demonstrates the practice of the present teachings in alongitudinal study of RA, and the predictive power of DAI scores totrack changes in a subject's DAS28 scores over time. The DAI score thusprovides a quantitative measure to monitor changes in subject diseaseactivity and response to treatment.

Experimental Design, Biomarker Selection and Quality Control of AssayData

Analyzing data obtained from multiple time points for a subject is notonly useful in monitoring changes in that subject's disease activity,but can also be useful in increasing the prediction accuracy of a DAIformula. The objective of this study was to develop, validate, andcompare biomarker-based models (single time point and longitudinal) thatmeasure disease activity in RA subjects over time, in order todemonstrate that the performance of the longitudinal model is betterthan cross-sectional.

For the purpose of the longitudinal study described herein, a subjectgroup was selected from the BRASS cohort. See Example 1 for a generaldescription of the BRASS cohort. Note that the specific subject samplesused in this study were different from those analyzed in Example 1.(Therefore, this longitudinal study can also serve as an independentcohort validation for the study described in Example 1.) A total of 255samples were obtained from the annual physician visits of 85 RA subjects(at years 1, 2 and 4), and were used for this study. The cohort for thisstudy had the following characteristics: 91% female, 62% CCP positive,64% RF positive, 4% smokers, 48% on MTX, 64% on non-biologic DMARDs, 43%on biologic DMARDs, and 27% on steroids. Additionally, the mean age ofthe cohort was 59 years (SD+/−12.7), with a minimum age of 29 and amaximum age of 85. The mean DAS28-CRP for this cohort was 4.1(SD+/−1.7), with a wide distribution of DAS28-CRP scores (minimum of 1.2and a maximum of 8.2).

Twenty-one biomarkers selected from the DAIMRK set were assayed in amultiplex format or an ELISA format. (Various suppliers were identifiedthrough a screening and optimization process prior to the study; e.g.,Millipore, R & D Systems, Meso Scale Discoveries, and various ELISAassay suppliers.) All assays were performed following the manufacturer'sinstructions with cohort samples randomly assigned (or the equivalent)to the sample positions on the plate layouts. Four pooled sera (Normal,RA, SLE and OA) were included in each 96-well plate as process controls.All samples were assayed at least in duplicate. Seven-point calibrationcurves were constructed for each biomarker, to accurately determine themeasurable range of test sera. See Example 3 for a discussion of howstudy assay data were qualified.

Performance of the DAI Model in Tracking Longitudinal Changes in DAS28

See Example 1 for an explanation of selected statistical models used toconstruct the relationship between DAI and DAS28 scores. In addition,DAI models were also built based on longitudinal hierarchical linearmethods (HLM), which incorporated all timepoint information. The HLMinclude both time-variant and time-invariant variables.

Results

The correlation between change of DAI and change of DAS28 between twotime points was r=0.56 in the dataset described in this example, wherethe DAI model was built from a single-timepoint penalized regressionmodel with cross-sectional data from the BRASS cohort described inExample 1. The correlation increased to 0.69 when a longitudinal HLM wasbuilt from the data described in this example and tested on the Taylorcohort described in Example 5.

This study demonstrates that a DAIMRK-derived algorithm developed inboth cross-sectional and longitudinal analyses was a strong predictor ofdisease activity over time. These results further demonstrate that thebiomarker algorithm utilized in this study has a high level of accuracyand is robust with respect to sampling over time.

Example 3 Classification of Subjects by DAI Score

Example 3 demonstrates the use of a DAI score to classify subjectsaccording to disease activity. The study was conducted with 182 samplesfrom the BRASS cohort (see Example 1), and 140 samples from a cohortestablished by the Oklahoma Medical Research Foundation (the OMRFcohort). The appropriate Ethics Committee approval was obtained for thestudy, and all subjects gave informed consent. Since 2007, more than 800subjects with confirmed RA have been enrolled in OMRF cohort. Allsubjects fulfilled the American College of Rheumatology criteria for RA.The cross-sectional study collected medical or clinical information fromall subjects, comprising disease activity scores, radiological results,subject health status and other clinical assessments. Blood samples werecollected during office visits. The subjects from the BRASS cohort forthis study had the following characteristics: 86% female, 65% CCPpositive, 70% RF positive, 5% smokers, 60% on MTX, 72% on non-biologicDMARDs, 55% on biologic DMARDs, and 23% on steroids. Additionally, themean age of the subjects of the BRASS cohort was 58 years (SD+/−14.3),with a minimum age of 22 and a maximum age of 94. The mean DAS28-CRP forthe subjects of this cohort was 3.2 (SD+/−1.2), with a minimum of 1.2and a maximum of 7.5. The subjects from the OMRF cohort for this studyhad the following characteristics: 75% female, 60% CCP positive, 98% RFpositive, 22% smokers, 63% on MTX, 81% on non-biologic DMARDs, 49% onbiologic DMARDs, and 32% on steroids. Additionally, the mean age of thesubjects of this cohort was 60 years (SD+/−13.1), with a minimum age of26 and a maximum age of 84. The mean DAS28-CRP for the subjects of thiscohort was 5.2 (SD+/−1.5), with a minimum of 2.2 and a maximum of 8.2.

DAIMRK biomarker assays and assay data quality control were performed asdescribed in Example 1.

A cut-off of DAI=3 best separates the low DAS (DAS<2.67) and high DAS(DAS>2.67) subjects, at an accuracy rate of >0.8. See FIG. 14. When theDAS threshold is set to 4.0 instead of 2.67, DAI also reached theaccuracy rate of 0.8. See FIG. 15.

This study demonstrates that a DAI algorithm derived from the DAIMRK setof biomarkers can be used to classify subjects into well-establishedlevels of disease activity, relative to the gold-standardclinically-based measure, the DAS28.

Example 4 Use of DAI to Distinguish Subjects with RA from Unaffected,Healthy Controls

Example 4 demonstrates the use of the DAI score in the diagnosis of RA,by discriminating subjects with RA from unaffected, healthy controls.

Data from 24 healthy control subjects and 31 subjects diagnosed with RAwere examined to determine whether mean DAIMRK biomarker levels weredifferent between the two groups. Twenty-one biomarkers selected fromthe DAIMRK set were assayed in a multiplex format or an ELISA format.Assay suppliers were previously identified through a screening andoptimization process (e.g., Millipore, R & D Systems, Meso ScaleDiscoveries, and various ELISA assay suppliers). All assays wereperformed following the manufacturer's instructions, with cohort samplesrandomly assigned (or the equivalent) to the sample positions on theplate layouts. Four pooled sera (normal, RA, SLE and OA) were includedin each 96-well plate as process controls. All samples were assayed atleast in duplicate. Seven-point calibration curves were constructed foreach biomarker protein, for accurate determination of the measurablerange of test sera. See Example 3 for a discussion of how study assaydata were qualified.

Statistical Analysis

Statistical analyses of data included the t-test, random forests,boosted trees, and KNN. Boosted Trees models are based upon the idea ofcomputing a sequence of trees, where each successive tree is built bypredicting the residuals of the preceding tree. Put another way,boosting will generate a sequence of classifiers, where each consecutiveclassifier in the sequence is an “expert” in classifying observationsthat were not well-classified by those preceding it.

The univariate statistical analysis in this Example was performed usinga two-sample t-test with Satterthwaite adjustment. The resulting datashowed a right-skewed distribution, so a logarithmic transformation wasused to correct for the skew, and a numeric value of 1 was added toavoid the asymptotic tail of the resulting logarithmic function betweenthe numeric values of 0 and 1. The univariate analyses indicated thatthe relative levels of CCL22, CRP, IL6, IL8, keratan sulfate, andTNFSF1A were significantly different between healthy (Control)individuals and RA subjects. See Table 2.

TABLE 2 DAIMARK variable RA Control p-value CCL22 3.71 (0.19) 3.47(0.15) 1.14E−06 CRP 4.55 (0.61) 4.22 (0.47) 0.027294 IL6 0.98 (0.37)0.82 (0.2) 0.049 IL6R 4.23 (0.18) 4.3 (0.09) 0.053 IL8 1.18 (0.26) 1.04(0.15) 0.015925 keratan sulfate 2.28 (0.08) 2.44 (0.08) 2.21E−09TNFRSF1A 2.9 (0.19) 3.03 (0.15) 0.007447

Multivariate Analysis

The Random Forest algorithm was provided with the DMARK variables fromTable 2 and samples were split, 43% into the Test set and 56% into theTraining set. The Training set variables were ranked based upon theirrelative importance in the model. Relative importance is based on thedegree to which each variable contributes to improving the model fit.See R A Belk, “Statistical Learning from a Regression Perspective,”Springer, 2008, p. 213. See Table 3.

TABLE 3 Variable Importance CCL22 1 keratan sulfate 0.748 IL6R 0.707TNFRSF1A 0.452 IL8 0.438 IL6R 0.41 CRP 0.24

The Training set data showed 96.8% accuracy and the Test set data showed87.5% accuracy, as measured by ability to discriminate subjects with RAfrom unaffected healthy controls. The test confusion matrix specifiesthe error (confusion) in the actual versus predicted classification. SeeTable 4.

TABLE 4 Test confusion* matrix Training confusion* matrix ActualPredicted Actual Predicted RA 14 11 17 17 Control 10 10 14 13 Total 2431 Accuracy 87.5% 96.8%

Here “Predicted RA” refers to samples from subjects that were predictedto have RA and actually did, while “Predicted Control” refers to samplesfrom subjects that were predicted to be healthy and actually were. Thusin the Test confusion matrix shown in Table 4, of the 24 samples tested,14 of the RA samples were correctly predicted to be RA positive andthree were incorrectly predicted to be healthy, while all 10 controlsamples were correctly predicted to be healthy. The accuracy then iscalculated as: (number Predicted RA that is Actual RA)+(number PredictedControl that is Actual Control)÷total number samples; or, for the Testconfusion matrix, (11+10)÷24=0.875, and for the Training confusionmatrix, (17+13)÷31=0.968.

The boosted tree algorithm was given the DAIMRK variables in Table 2 andthe samples split 33% into the Test set, and 64% into the Training sets.The Training set variables were ranked based upon their relativeimportance in the model. See Table 5.

TABLE 5 Variable Importance keratan sulfate 1 CCL22 0.95 CRP 0.91TNFRSF1A 0.84 IL6R 0.77 IL6R 0.72 IL8 0.59

The Training set data showed 100% accuracy and the Test set data showed83.3% accuracy. See Table 6.

TABLE 6 Test confusion matrix Training confusion matrix Actual PredictedActual Predicted RA 9 7 22 22 Control 9 8 15 15 Total 18 37 Accuracy83.3% 100%

Results

Using stored blood samples from RA and healthy subjects, relationshipswere examined between the protein serum levels of different DAIMRKbiomarkers related to immune activation and inflammatory response. Themean DAIMRK biomarker levels were different between the two groups ofsubject. Additionally, the levels of CCL22, CRP, IL6, IL8, keratansulfate, and TNFSF1A were significantly different between healthysubjects and RA subjects. These results would indicate that as RAdisease progresses, additional pathological mechanisms are activated totrigger the onset of clinical symptoms.

Example 5 Assessment of Response to Therapy Using DAI Scores

This example demonstrates that the DAI score is useful in assessing asubject's response to a single therapy, and in comparing subjects'responses to two therapies. The hypothesis that the DAI score issignificantly associated with a subject's response to infliximabtreatment was tested, as was the hypothesis that the DAI score isassociated with differences in response to two therapies.

Serum samples and clinical and imaging data were examined from 24subjects (the Taylor cohort), who were followed in a two-year blindedstudy to compare a therapeutic regimen of MTX and infliximab against atherapeutic regimen of MTX alone, in aggressive early RA. Placebo armsubjects were switched to methotrexate and infliximab after one year.Subjects were evaluated by ultrasound at 0, 18, 54 and 110 weeks, andscored for synovial thickening and vascularity by power Doppler area(PDA). Radiographic examination to determine van der Heijde modifiedSharp (vdH-Sharp) scores was carried out at 0, 30, 54 and 110 weeks.DAS28 scores were obtained at office examinations carried out everythree to five weeks over the two-year study period. DAIMRK biomarkerlevels were determined in blood samples from all 24 subjects collectedat 0, 6, 18, 54 and 110 weeks.

Characteristics of the subjects of the Taylor cohort were as follows:the mean age of the placebo+MTX subgroup was 51 years (SD+/−14.0), theinf+MTX subgroup was 55 years (SD+/−11.8); the mean weight in kg of theplacebo+MTX subgroup was 71.1 (SD+/−13.2), the inf+MTX subgroup was 67.9(SD+/−16.1); the mean disease duration of the placebo+MTX subgroup was1.64 years (SD+/−0.63), the inf+MTX subgroup was 1.33 (SD+/−0.64).

To show that DAI score is significantly associated with a subject'sresponse to infliximab treatment, each subject's DAI score beforeinfliximab treatment (year 0, week 0) was compared to his/her scoreafter one year of infliximab treatment (year 1, week 52). Row A of Table7 shows the results of a test (paired t-test) of the difference betweenthe DAI scores at year 0 and year 1 for 12 subjects receiving infliximab(inf). The DAI scores were computed from the model built from BRASSsubjects, described elsewhere herein. The t-stat is the value of thetest statistic, t, for which a p-value can be calculated using the Tdistribution.

TABLE 7 t-stat p-value A Change in inf, year 0 to 1 −2.69981 0.007764 BDifference MTX and −1.41064 0.093483 difference MTX, year 0 to 1

As Table 7 shows, the paired t-test is significant (p=0.007764), thusdemonstrating that the DAI score changes significantly followinginfliximab treatment.

To show that the DAI score is useful in assessing differences insubjects' response to two therapies, the DAI scores of subjectsreceiving infliximab treatment were compared to the DAI scores ofsubjects receiving MTX treatment. The DAI scores of weeks 0 to 52 weresubtracted within both MTX and infliximab subjects. Twelve datapoints(or DAI score differences) were obtained for each treatment group. Thena non-paired t-test (n=12 for each group) was used. Row B of Table 7shows the results of the t-test for the difference in DAI scores ofinfliximab subjects and DAI scores of MTX subjects. The t-test shows atrend to significance (p=0.09). A sample size of greater than twelveobservations would be expected to yield a significant p-value for thisdifference.

This example demonstrates that the DAI score is useful in assessing asubject's response to a single therapy, and that the DAI score is usefulin comparing subjects' response to two therapies.

Example 6 Correlation of DAI Scores with Clinical Measures of Erosion

This example demonstrates that DAI scores track joint erosion, with astrong correlation between DAI scores and radiographic changes insubjects, based on changes in Sharp scores from X-ray imaging andchanges in measures of joint damage (i.e., synovial thickening,vascularity, and intra-articular blood flow) assessed by power Doppler(PD) ultrasonography. Synovial vascularization and mononuclear cellinfiltration are known to be characteristics of RA synovitis. See P.Taylor et al., Arth. Rheum. 2004, 50(4):1107-1116. This exampledemonstrates that DAI scores can provide the current rate of jointdestructive processes in subjects, and correlate with ultrasoundobservations of subclinical synovitis. Thus, DAI scores are a powerfulcomplementary approach to identify subjects at highest risk ofaccelerated bone and cartilage damage.

The samples used in this example were the Taylor cohort, describedabove. See Example 5. Clinical measures of erosion were assessed usingtwo radiographic modalities: X-ray and ultrasound. X-rays of hands andfeet taken at 0, 30, 54 and 110 weeks provided van der Heijde modifiedSharp scores. All subjects had erosions at baseline (week 0), butexperienced a wide range of changes in total Sharp scores (TSS) over thecourse of the study (median change 6.25, inter-quartile range 4-14.5).Ultrasound studies provided three measures of joint damage: colorDoppler area (CDA), synovial thickening (SYN), and erosion score (ES).Blood samples from all 24 subjects were collected at 0, 6, 18, 54 and110 weeks, and were used to measure the levels of protein biomarkersselected from the ALLMRK set, described above.

Correlation coefficients between the DAI scores and the three ultrasoundmeasures observed were calculated. The DAI score was calculated for eachsubject at each given timepoint, and those DAI score values were thenpaired with the ultrasound scores for that subject at same timepoints.The 24 subjects had ultrasound scores at timepoint 0, 18, 54, and 110weeks. The correlation (Cor) was computed as Cor(DAL_kt, ultrasound_kt),where k is 1, . . . , 24 and t=0, 18, 54, 110. Thus, 24 subjects*4timepoints per subject=96 datapoints total were used in computing theCor. The DAI score was correlated to all three ultrasound measures(p<0.05).

Table 8 shows the correlation between DAI scores and Sharp scores. TheDAI model was built from a separate cohort of subjects (BRASS) toprevent over-fitting. The DAI scores were computed across all 24subjects at week 6, when therapeutic effect was observable. The resultsin Table 8 were computed as follows: (a) build DAI model from BRASScohort of subjects; (b) calculate the DAI score in Taylor cohort ofsubjects (all 24) using week 6 data; (c) use leave-one-outcross-validation procedure, and for each 23 subjects (i) build alongitudinal model using the week 6 DAI score to predict rate of changein total Sharp score (TSS) (i.e., change of TSS/week), (ii) calculatethree Sharp score rates of change (i.e., 0-54 weeks, 0-110 weeks, and54-110 weeks) for the left-out subject, (iii) calculate three estimatedTSS rates of change (0-54 weeks, 0-110 weeks, and 54-110 weeks) for theleft-out subject, from (i); (d) after obtaining all the estimated TSSchanges for each subject, calculate the correlation between the actualTSS rate of change and the estimated one based on the DAI scores for all24 subjects. The correlations were calculated for each interval (e.g.,0-54 weeks) separately.

TABLE 8 Interval Correlation Week 0-54 0.769 Week 0-110 0.737 Week54-110 0.567

These results demonstrate that DAI scores are correlated with clinicalmeasures of erosion, as determined by X-ray (i.e., Sharp scores) andultrasound observations of subclinical synovitis in subjects' joints.

Example 7 Association of DAI with DAS28 Scores in Another Large ClinicalCohort

Example 7 demonstrates the transformation of observed biomarker levelsinto a DAI score by various statistical modeling methodologies, whichDAI score serves as a quantitative measurement of disease activity thatcorrelates well with observed DAS28, as for measuring the extent ofsubject inflammation status and disease activity at any singletimepoint. This example also demonstrates the selection of a particularset of 23 biomarkers, all members of the DAIMRK set; namely, SAA1, IL6,TNFRSF1A, VEGFA, PYD, MMP1, ICAM1, calprotectin, CHI3L1, MMP3, EGF,IL1RN, VCAM1, LEP, RETN, CRP, IL8, APOA1, APOC3, CCL22, IL1B, IL6R andIL18. Certain embodiments of the present teachings comprise utilizingthese biomarkers from the DAIMRK set of biomarkers for determining a DAIscore with significant correlation with disease activity status.

Samples were obtained from the Computer Assisted Management in EarlyRheumatoid Arthritis Study (CAMERA). From 1999-2003, all earlyrheumatoid arthritis patients (i.e., disease duration of one year orless) who fulfilled the 1987 revised American College of Rheumatology(ACR) criteria for rheumatoid arthritis were asked to participate inthis two-year randomized, open-label prospective multicentre strategytrial. As a result, 299 patients were studied. Patients visited theoutpatient clinic of one of the six rheumatology departments in theregion of Utrecht, the Netherlands, collaborating in the UtrechtRheumatoid Arthritis Cohort study group. Inclusion criteria were thatpatients must have exhibited symptoms for less than one year, with agegreater than 16 years. Exclusion criteria were the previous use ofglucocorticoids or any DMARD, use of cytotoxic or immunosuppressivedrugs within a period of three months before inclusion, alcohol abuse,defined as more than two units per day, and psychological problems,which would make adherence to the study protocol impossible. At baselineall patients were monitored for medical conditions that would interferewith MTX usage. This screening included a chest X-ray, liver enzymes,albumin, hepatitis serology, serum creatinine and complete blood count.An independent person performed randomization in blocks of nine perhospital. The medical ethics committees of all participating hospitalsapproved this study, and all patients gave written informed consentbefore entering the study.

The cohort for this study had the following characteristics: 69% female,68% CCP positive, 74% RF positive, 100% on MTX, 100% on non-biologicDMARDs, and 0% on biologic DMARDs. Additionally, the mean age of thecohort was 52 years (standard deviation (SD)+/−14.7), with a minimum ageof 17 and a maximum age of 78. The mean DAS28-CRP for this cohort was5.0 (SD+/−1.9), with a minimum of 0.9 and a maximum of 8.4.

A subpopulation of 72 subjects was selected from the CAMERA cohort forthis Example. All 72 patients were represented by baseline (time 0)visits and samples, and 48 were also represented by six-month visits andsamples. Within the visits selected, a wide distribution of DAS28-CRPscores were represented, ranging from a minimum of 0.96 to a maximum of8.4.

Assays were designed, in multiplex or ELISA format, for measuringmultiple disease-related protein biomarkers selected from the ALLMRKset, as that set is described herein. These assays were identifiedthrough a screening process and were extensively optimized prior toassaying the CAMERA samples. SAA1, IL6, TNFRSF1A, VEGFA, MMP1, ICAM1,calprotectin, CHI3L1, MMP3, EGF, VCAM1, LEP, RETN, CRP, IL8, APOA1,APOC3, CCL22, IL1B and IL6R were measured using the MESO SCALEDISCOVERY® (MSD) platform. IL8 and IL1RN were measured with ELISAtechnology from R&D Systems, and PYD was measured with ELISA fromQuidel.

All assays were performed following the manufacturer's instructions,with cohort samples randomly assigned (or the equivalent) to the samplepositions on the plate layouts. Four pooled sea (from normal, RA, SLEand osteoarthritis (OA) subjects) were included in each assay plate asprocess controls. All samples were run at least in duplicate.Seven-point calibration curves were constructed for each biomarker foraccurate determination of the measurable range of test sera.

Prior to statistical analyses, all assay data were reviewed forpass/fail criteria as predefined by standard operating procedures onparameters, including inter-assay CV, intra-assay CV, percent of sampleswithin the measurable range of the calibration curve, and four serumprocess controls within the range of the calibration curve. Thebiomarker values that were not in the measurable range of thecalibration curves were marked as missing data, and imputed by thelowest/highest detected value across all the samples within a givenbiomarker assay. No imputation was performed for the univariateanalyses. For multivariate analysis, missing data imputation methodscommonly used in microarray expression data and well known in the artwere used. See, e.g., R. Little and D. Rubin, Statistical Analysis withMissing Data, 2nd Edition 2002, John Wiley and Sons, Inc., NJ.Biomarkers were excluded from analysis where more than 20% of the datawere missing, and the remaining data were imputed by the KNN algorithm(with k=5 nearest neighbors).

Univariate Analysis

Biomarker assay data were normalized across each plate beforecorrelations were calculated between individual proteins andmeasurements were transformed into DAI scores. Associations werecalculated between the DAI scores and DAS28-CRP scores, swollen jointcounts, TJCs, or CDAI. The correlation results were then compared usingunivariate analysis. See Table 9, results of univariate analyses forseveral DAIMRK biomarkers in the CAMERA training set.

The False Discovery Rate (FDR) was used as multiple testing correction,according to the following: let k be the largest i for which pi≦i/m*αreject all Hi, i=1, . . . , m. As will be clear to one of skill in theart, where the DAIMRK biomarker is significantly associated with the DASscore, then the q-value is small. A parametric correlation test was alsoperformed, using the parametric test H_(i): ρ_(i)=0, and the statisticgiven by

$t = {\frac{{r\left( {n - 2} \right)}^{1/2}}{\left( {1 - r^{2}} \right)^{1/2}}.}$

Covariation and multicolinearity between all variables were evaluated;i.e., for both clinical data and biomarkers. If a strong correlation wasseen to exist between biomarkers, it indicated that multicolinearityshould be taken into account during the model building process. If astrong association was detected between baseline clinical variables andbiomarkers, it was determined that further evaluation was needed. ANOVAand Spearman correlations, along with p-values and FDR, were used toexamine associations between all continuous clinical variables (withoutDAS28 scores) and biomarkers.

TABLE 9 Correlation Nominal DAIMRK coefficient p-value IL6 0.693 0 CRP0.685 0 SAA1 0.658 0 calprotectin 0.557 0 MMP3 0.509 0 IL8 0.466 0 IL1B0.454 0 CHI3L1 0.423 0 MMP1 0.364 0 TNFRSF1A 0.363 0 VEGFA 0.293 0.001ICAM1 0.23 0.012 pyridinoline 0.228 0.013 RETN 0.219 0.016

Multivariate Analysis

Several multivariate modeling methods were considered. In general, thelinear penalized regression model was determined to perform the best.

Model 1: Forward Stepwise Ordinary Least Square Regression

See Example 1 for a description of the forward stepwise ordinary leastsquare regression model.

Model 2: Penalized Regressions

See Example 1 for a description of the penalized regressions model.

Coefficients Representative of a DAI Model

The following coefficients represent the terms of the respective DAImodels: DAI_(k)=Σβ_(i)x_(ik), where DAI_(ik) is the calculated DAI forthe kth subject, x_(ik) represents the standardized ith biomarkerconcentration for the kth subject (usually log transformed andplate-to-plate normalized), and β_(i) is the coefficient for the ithbiomarker.

Cross-Validation

A random subset of 70% of the total study population was selectedwithout replacement. The model was fitted using this subset, thenevaluated against the remaining 30% of the study population, using AUCand correlation. Cross-validation was repeated 100 times, and theresulting accuracy estimates were averaged to predict futureperformance.

Results

The DAI score in the present example was computed using the followingformula:DAI=(−16.16)−(0.06*calprotectin)+(0.22*CHI3L1)+(1.19*ICAM1)+(2.77*IL6)+(0.73*MMP1)−(0.83*MMP3)+(1.03*pyridinoline)+(1.18*SAA1)+(2.44*TNFRSF1A)+(0.33*VEGFA).

This formula exhibited a correlation of 0.65 and AUC of 0.84 inpredicting DAS28 in the independent cohort, CAMERA.

The analyses demonstrated that the DAI scores correlate well with DAS28scores, and also discriminate between subjects with high and low DAS28scores, thus allowing for classification of subjects by diseaseactivity.

Correlations of the DAI scores with DAS28 were r=0.75 to r=0.78, asestimated using 100 test set cross-validations. Specifically, the DAS28correlation of the DAI score derived using the Lasso method was 0.776,the DAS28 correlation of the DAI score derived using the Elastic Netmethod was 0.762, and the DAS28 correlation of the DAI score derivedusing the forward variable selection method was 0.746. (Forwardselection is a method of finding the “best” combination of variables bystarting with a single variable, that which results in the best fit forthe dependent variable Y, and increasing the number of variables used,step by step, testing all combinations of the original variable with theremaining variables in order to find the “best” pair of variables,continuing until either all variables are used up or some stoppingcriterion is met.)

These results show that the DAI scores derived using each of thesemodeling methods, and using different subsets of the protein biomarkers,all yield good correlation with DAS28 scores.

DAI scores can also be used to discriminate between subjects with highand low DAS28 scores, as demonstrated by the value of the area under theROC curve, estimated using 100 cross-validation test sets. For subjectsdichotomized on a DAS of 4.1, which is the median DAS value of thisstudy, the area under the ROC curve for the DAI score derived using theLasso method was 0.896. The area under the ROC curve for the DAI scorederived using the Elastic Net method was 0.881. These results show thatthe DAI scores derived using each of these methods all yield good areasunder the ROC curves for discriminating subjects with high and low DAS28scores.

Example 8 Association of DAI Scores with DAS28 Scores by AUC is notDependent on Subgroup

Example 8 demonstrates that the correlation of DAI scores with DAS byAUC, and thus the usefulness of DAI scores to classify subjects bydisease activity, are not significantly affected by subjectsubgroupings, such as by CCP status, sex, age, etc.

The performance of the 10-marker DAI algorithm (described in Example 7)relative to DAS28-CRP was further evaluated in patient subgroups fromthe CAMERA cohort (see Example 7 for a description of the CAMERA study)defined by several major clinical variables; namely, sex, RF status, CCPstatus, and age. Table 10 presents the correlation and classification(AUC) results of this analysis.

TABLE 10 AUC Sex (M; F) 0.828 0.849 RF status (Neg; Pos) 0.8 0.852 CCPstatus (Neg; Pos) 0.820 0.837 Age (under 53; over 53) 0.858 0.851

This analysis indicates that the capability of DAI scores to classifysubjects by disease activity, as demonstrated by AUC values, are notsignificantly affected by the subject subgroupings of sex, RF status,CCP status, and age.

Example 9 Change in DAI Scores not Strictly Correlated with SingleBiomarker Levels

Example 9 demonstrates that changes in subjects' disease activity, asevidenced by changes in their DAI or DAS scores between first and secondclinical visits, do not strictly correlate with changes in the levels ofthe single biomarker CHI3L1. In other words, univariate analysis of theDAIMRK biomarker CHI3L1, which is positively weighted in an exemplaryDAI algorithm (see, e.g., example 7), indicated that despite itspositive weight, an increase in CHI3L1 level does not statisticallycorrelate with an increase in disease activity, and vice versa.

The Index for Rheumatoid Arthritis Measurement (INFORM) study is a largemulti-center observational study of the North American RA population.Patients were recruited between April and September 2009 from 25 sitesin the U.S. and Canada. Inclusion criteria were: age>18 years with adiagnosis of RA made by a board-certified rheumatologist. Patientsconcurrently enrolled in a therapeutic drug trial involving a biologicagent and a placebo arm were excluded. At their first study visit, 512patients were selected for biomarker analysis. The average age of thesepatients was 58.9 years (range 20-91), and 76% were female. The mean SJCand TJC were 4.28 and 5.49, respectively. Of these 512 patients, 128were tested for CHI3L1 at both the first and second study visits, whichwere separated by around 3 months. Of these patients, 53% had increasedDAI values at the second visit. Among the patients with increased DAIvalues, 57% also demonstrated an increase in CHI3L1 values. See Table11.

TABLE 11 No. patients DAI decreased/stayed No. patients same DALincreased No. patients CHI3L1 36 29 decrease/stayed same No. patientsCHI3L1 24 39 increased

These results indicate that in the example of the DAIMRK biomarkerCHI3L1, weighted positively in the DAI algorithm of Example 7, forexample, an increase in CHI3L1 level does not necessarily correlate withan increase in RA disease activity, as measured by DAI, and vice versa.

The same holds true when the change in levels of CHI3L1 is compared tochange in disease activity as measured by DAS. In a study of the INFORMcohort, 44% of the patients demonstrated an increase in DAS values insecond visits, among which 43% demonstrated an increase in CHI3L1values. See Table 12.

TABLE 12 No. patients No. DAS patients decreased/ DAS stayed sameincreased No. patients CHI3L1 33 32 decreased/ stayed same No. patients39 24 CHI3L1 increased

In another analysis, the change in CHI3L1 levels from the first tosecond visit was compared to DAI change, where the DAI change from visit1 to visit 2 was at least by a magnitude of 10%. The results are shownin Table 13.

TABLE 13 No. patients DAI No. patients DAI decreased by <= 10% increasedby >10% No. patients 58 7 CHI3L1 decreased/ stayed same No. patients 4419 CHI3L1 increased

These results demonstrate that among patients demonstrating a DAIdecrease of at least 10% in the subsequent visits, 43% of thesedemonstrated an increase in CHIL1 levels.

Changes in CHI3L1 levels were likewise analyzed against changes in DASvalues, where DAS changed by at least 10%. Results from the INFORM studyshowed that among all patients where DAS increased by at least 10%, only41% also showed an increase in CHI3L1 level. See Table 14.

TABLE 14 No. patients No. patients DAS decreased DAS increased by by <=10% >10% No. patients 42 23 CHI3L1 decreased/ stayed same No. patients47 16 CHI3L1 increased

Taken together, these results demonstrate that in the example of theDAIMRK biomarker CHI3L1, weighted positively in the DAI algorithm ofExample 7, for example, an increase in CHI3L1 level does not necessarilycorrelate with an increase in RA disease activity, as measured by DAI orby DAS, and vice versa.

Example 10 Performance of Univariate Models Across Various Cohorts

This example demonstrates that the predictive value univariate (singlebiomarker) models are weaker across various cohorts than are themultivariate models of the present teachings.

The ability of each single DAIMRK biomarker to predict disease activitywas analyzed for the cohorts indicated in Table 15, and the correlationvalues obtained. (For a description of BRASS, see Example 1; for CAMERA,see Example 7; for INFORM, see Example 9).

TABLE 15 BRASS CAMERA INFORM corre- corre- corre- DAIMRK lation p-valuelation p-value lation p-value calprotectin 0.42  0    0.557 0    0.2510    CCL22 0.167 0.034  N/D* N/D 0.123 0.005 CHI3L1 0.498 0    0.4230    0.207 0    CRP 0.803 0    0.685 0    0.421 0    EGF −0.218   0.005N/D N/D N/D N/D ICAM1 0.366 0    0.23  0.012 0.186 0    ICTP N/D N/D N/DN/D 0.162 0    IL1B N/D N/D 0.454 0    0.161 0.001 IL1RA 0.31  0    N/DN/D 0.183 0    IL6 0.597 0    0.693 0    0.325 0    IL6R 0.224 0.004 N/DN/D 0.132 0.003 IL8 N/D N/D 0.466 0    0.139 0.002 LEP 0.176 0.023 N/D0.151 0.001 MMP1 0.411 0    0.364 0    0.135 0.003 MMP3 0.562 0    0.5090    0.189 0    pyridinoline 0.379 0    0.228 0.013 0.115 0.01  RETN0.236 0.002 0.219 0.016 N/D N/D SAA1 0.746 0    0.658 0    0.318 0   TNFRSF1A 0.506 0    0.363 0    0.201 0    VCAM1 0.291 0    N/D N/D N/DN/D VEGFA 0.43  0    0.293 0.001 0.17  0    *N/D: “Not Done”

As is evident from this table, these univariate markers cannot be usedwith consistency to predict disease activity across cohort populations.By comparison, the 10-marker panel of Example 7 demonstrated, in CAMERA,a correlation of 0.65 and an AUROC of 0.84; in BRASS, representativeLasso models achieved an average correlation of 0.76 and AUROC of 0.88;and, in INFORM, representative Lasso models in the 512 samples achievedan average correlation of 0.44 and AUROC of 0.67 in cross-validation.

Example 11 Alternative Modeling for Deriving DAI Score

This example demonstrates another, alternative method of deriving aDisease Activity Index score, based on a dataset of quantitative datafor biomarkers. In this example, a DAI score is determined from thebiomarker data using an interpretation function that is based on a setof predictive models, where each predictive model is predictive of acomponent of the DAS28-CRP, in this example TJC, SJC and patient globalhealth assessment (GHA).

DAI Algorithm Development and Evaluation Training Data

A DAI algorithm was trained using clinical and biomarker data forpatients in the InFoRM and BRASS studies. The InFoRM study (Index ForRheumatoid Arthritis Measurement) is a multi-center observational studyof the North American RA population. The patients used in algorithmtraining were recruited between April and September 2009 from 25 sitesin the U.S. and Canada. Inclusion criteria were: age>18 years with adiagnosis of RA made by a board-certified rheumatologist. Patientsconcurrently enrolled in therapeutic drug trials involving a biologicagent and a placebo arm were excluded. The study includes three visitsfor each patient, each with clinical data and biological samplecollection, at approximately three-month intervals.

BRASS is an observational study of approximately 1,000 RA patientsreceiving care at the RB Brigham Arthritis and Musculoskeletal DiseasesClinical Research Center, at the Brigham and Women's Hospital, Boston.Mass. Inclusion criteria were: age>18 years with a diagnosis of RA madeby a board-certified rheumatologist. The study includes annual visitswith clinical data and biological sample collection, and patientquestionnaires between visits.

The first data set used in training consisted of visit 1 data for 512InFoRM patients. The 512 patient visits were chosen to have clinicalcharacteristics representative of the entire study population at thetime of selection, and also to have been evaluated by a limited numberof joint assessors. The number of joint assessors was limited to 12 sothat assessor-specific biases could be evaluated and taken into accountin algorithm development. The average age of these patients was 58.9years (range 20-91), and 76% were female. The mean SJC and TJC were 4.28and 5.49, respectively.

Assays for 25 candidate biomarkers were run on serum from the 512 InFoRMvisits. Those biomarkers were SAA1, IL6, TNFRSF1A, VEGFA, PYD, MMP1,ICAM1, calprotectin, CHI3L1, MMP3, EGF, IL1RA, VCAM1, LEP, RETN, CRP,IL8, APOA1, APOC3, CCL22, IL1B, IL6R, IL8, keratan sulfate and ICTP. Allthe biomarker assays were run on the Meso Scale Discovery (MSD®)platform. See Example 1 for specifics of biomarker assay development andevaluation.

The biomarkers were prioritized based on (1) univariate association withdisease activity, (2) contribution to multivariate models for diseaseactivity, and (3) assay performance.

The assays for 20 candidate biomarkers were run in a second set ofpatient samples, comprising 167 samples from BRASS and 29 from InFoRM.These 20 candidate biomarkers were SAA1, IL6, TNFRSF1A, VEGFA, PYD,MMP1, ICAM1, calprotectin, YKL40, MMP3, EGF, IL1RA. VCAM1, leptin,resistin, CRP, IL8, CCL22, IL1B and IL6R. The samples were selected toenrich the overall training data for extremes of disease activity, whilealso having good representation of patients with moderate diseaseactivity. Enriching for extreme phenotypes can result in improvedalgorithm training, as long as the resulting training population stillfully represents the types of patients on which the algorithm will usedin independent validation and intended use populations. The 167 BRASSsamples were intended to represent similar numbers of patients with low,moderate and high disease activity. The 29 InFoRM samples were selectedto represent patients with high disease activity, since low and moderateactivity patients were already well represented by the first 512training samples.

Data Analysis

Prior to statistical analyses, all assay data were reviewed forpass/fail criteria on parameters including inter-assay CV, intra-assayCV, percent of samples within the measurable range of the calibrationcurve, and four serum process controls within the range of thecalibration curve. The biomarker values that were not in the measurablerange of the calibration curves were marked as missing data, and imputedwith the lowest/highest detected value across all the samples within agiven biomarker assay during the data export process. If the intra-assayCV of the biomarker concentration, computed from two replicates, wasgreater than 30%, it was also considered missing and excluded fromunivariate analyses. For multivariate analysis, individual biomarkerswere excluded entirely if more than 20% of their data values weremissing, and other missing data were imputed by the KNN algorithm (withk=5 nearest neighbors). In the data used for algorithm training, nobiomarkers were excluded from multivariate analysis because they all hadless than 20% missing values. Concentration values were transformed as×0.1 prior to further analysis in order to make the distribution ofvalues for each biomarker more normal. This transformation has a similareffect to log transformation but avoids the generation of negativevalues. The transformed, imputed biomarker dataset is denoted asX_(n×m), where X is the protein data from n markers and m samples.

In univariate analysis, the Pearson correlations between the levels ofeach biomarker and disease activity measures including DAS28-CRP4,DAS28-ESR4, SJC, TJC, GHA, SDAI and CDAI were calculated.

In multivariate analysis, statistical models were developed by fivedifferent regression methods. In the first regression method (1),forward stepwise ordinary least square regression, the equation Y=Xβ+εapplies, where Y is the column vector with observed values, β is thevector of coefficients, and ε is the residuals. The forward selectionbegins with no variables in the model. Then, given a collection ofpredictors X, the one having the largest absolute correlation with theresponse Y is selected and a simple linear regression of Y on X1 isperformed. The residual vector is now orthogonal to X1, and is taken tobe the new response variable. The other predictors are then projectedorthogonally to X1 and the forward selection process is repeated.

In the second method (2), Lasso is used to prioritize biomarkers (basedon R² values) and to obtain a Lasso model. The “lasso” in this modelminimizes the residual sum of squares, subject to the sum of theabsolute value of the coefficients being less than a constant. Thismethod produces interpretable models and exhibits the stability of ridgeregression. See R. Tibshirani, J. Royal Stat. Soc. B 1996,58(1):267-288.

In the third method (3), the Elastic Net, mixtures of Lasso and ridgepenalties are applied. It encourages a grouping effect, where stronglycorrelated predictors segregate together, either tending to be in or outof the model together. See T. Zou, J. Royal Stat. Soc. B 2005,67(2):301-320. For each of the above three methods, the marker selectedat each step is recorded.

The fourth method (4) is Multivariate Response with Curds and Whey (CW)using ordinary least squares (OLS). See L. Breiman and J H Friedman, J.Royal Stat. Soc. B 1997, 59(1):3-54. This method takes advantage of thecorrelations between the response variables (e.g., components of DAS) toimprove predictive accuracy, compared with the usual procedure ofperforming an individual regression of each response variable on thecommon set of predictor variables X. In CW, Y=XB*S, where Y=(y_(kj))with k for the k^(th) patient and j for j^(th) response (j=1 for TJC,j=2 for SJC, etc.), B is obtained using OLS, and S is the shrinkagematrix computed from the canonical co-ordinate system. Hence, thisapproach will yield sub-models corresponding to each component of DAS.

The fifth method (5) is Curds and Whey and Lasso in combination(CW-Lasso). Instead of using OLS to obtain B as in CW, Lasso was used,and the parameters were adjusted accordingly for the Lasso approach.

The performance of the five regression methods was compared in 70/30cross validation (repeatedly training in a randomly selected 70% of thedata and testing in the remaining 30%). The number of markers in eachregression model was chosen by using nested 10-fold cross-validationonce the number of markers was selected for a given analysis method thebest-fitting model of that size was used to represent the method. In theCW approaches (methods 4 and 5), nested 10 fold cross validation wasused for each sub-model corresponding to each component of DAS. Themodels developed using the CW-Lasso method performed best overall. Thefollowing sections consist of results mainly using CW-Lasso approach.

The 20 candidate biomarkers examined in all training samples wereprioritized according to a number of criteria, including: strength ofassociation with disease activity and contribution to multivariatemodels: consistency of correlation with disease activity acrossfeasibility and training data sets; CRP was excluded from any sub-modelsfor TJC, SJC, and PGA both because it is included in the DAS28-CRP4 andbecause it did not increase sub-model prediction accuracy in independenttest samples (CRP is used, however, in the final DAI score calculationas part of the DAI formula); robust assay performance (IL1B was excludedfrom final modeling because its concentrations too frequently fall belowthe limits of detection of immunoassays): known drug effects (IL6R wasexcluded from final modeling because it is known to be strongly affectedby tocilizumab, independently of the effects of the drug on diseaseactivity); and, stability (IL8 was excluded from final modeling becauseits measurable levels are known to rise dramatically when serum samplesare not kept cold). These criteria led to 15 candidate biomarkers beingconsidered for inclusion in the final algorithm. See Table 16.

TABLE 16 Biomarker Functional Category calprotectin cytokines andreceptors CHI3L1 skeletal EGF growth factors ICAM1 adhesion moleculesIL1RA cytokines and receptors IL6 cytokines and receptors LEP hormonesMMP1 matrix metalloproteinases MMP3 matrix metalloproteinases PYDskeletal RETN hormones SAA1 acute phase response TNFRSF1A cytokines andreceptors VCAM1 adhesion molecules VEGFA growth factors

Training the Algorithm

While all data was used in prioritizing biomarkers, a subset was usedfor training the final algorithm. This subset was selected to have abroad range of disease activity levels, so that patients at all levelsof disease activity were well represented. A comparison was made of theperformance of models trained using: only BRASS samples (167 total);BRASS samples plus InFoRM samples (167+˜100) selected to have a uniformdisease activity distribution; or, BRASS samples plus InFoRM samples(167+˜100) with a disease activity distribution like that of the BRASSsamples.

The model performance was evaluated in an independent set of BRASS andInFoRM samples (70 total) set aside for this purpose. The DAS28-CRPdistribution of this independent test set was similar to that of paststudies (approximately normal). As shown below, correlation (r) to theDAS28-CRP and area under the ROC curve (AUROC) for predicting high andlow DAS using median cut off were higher when training used BRASSsamples plus “BRASS-like” InFoRM samples, although the differences werenot statistically significant. The following Table 17 uses the Lassoregression method.

TABLE 17 Training Set r AUROC BRASS only 0.53 0.68 BRASS + UniformInFoRM 0.54 0.69 BRASS + BRASS-like 0.55 0.71 InFoRM

For final training, the combination of BRASS plus “BRASS-like” InFoRMsamples was selected. The CW-Lasso regression method was chosen fordevelopment of the final algorithm because of its superior performancein cross validation within the training set and in testing using InFoRM512 patients and CAMERA patients (see below, DAI algorithm performance,for a description of algorithm testing in another cohort of samples). Inthe application of this method, the shrinkage matrix was applied to thepredictions of TJC and SJC. Ten-fold cross-validation indicated that thefollowing 13 markers were optimal for performance. See Table 18.

TABLE 18 Marker TJC SJC PGA calprotecin X CHI3L1 X X EGF X X X IL6 X X XLEP X X MMP1 X MMP3 X PYD X X RETN X SAA1 X X X TNFRSF1A X X VCAM1 X XVEGF1 X X

From this set, PYD and calprotectin were excluded due to elevated assayfailure rates. The remaining 11 biomarkers gave very similar algorithmperformance to the full set of 13. An algorithm was chosen forvalidation that was developed by CW-Lasso regression using this11-marker to estimate the DAS28-CRP in data from the BRASS+BRASS-likeInFoRM samples. The estimates of TJC, SJC and PGHA were combined with aCRP test result in a formula similar to that used to calculate theDAS28-CRP.

${DAS28CRP} = {{0.56\sqrt{TJC}} + {0.28\sqrt{SJC}} + {0.14\; {PGHA}} + {0.36\mspace{11mu} \log \mspace{11mu} \left( {\frac{CRP}{10^{6}} + 1} \right)} + 0.96}$${PDAS} = {{0.56\sqrt{IPTJC}} + {0.28\sqrt{IPSJC}} + {0.14\; {PPGHA}} + {0.36\mspace{11mu} \log \mspace{11mu} \left( {\frac{CRP}{10^{6}} + 1} \right)} + 0.96}$

Here IPTJC=Improved Prediction of TJC, IPSJC=Improved Prediction of SJC,PPGHA=Predicted PGHA, and PDAS is Predicted DAS28-CRP. (Details aredefined below; see Selected algorithm.) The DAI score is the result fromthis formula.

Table 19 demonstrates the correlation of the values predicted by thePDAS algorithm with actual values for TJC, SJC, PGHA and DAS28-CRP, inthe two cohorts studied, CAMERA and InFoRM.

TABLE 19 Study TJC SJC PGHA DAS28-CRP CAMERA 0.445 0.536 0.427 0.726InFoRM 0.223 0.328 0.388 0.53 (512 subject)

Selected Algorithm

The 11-marker+CRP Lasso model selected from the training process is asfollows:

PTJC=−38.564+3.997*(SAA1)^(1/10)+17.331*(IL6)^(1/10)+4.665*(CHI3L)^(1/10)−15.236*(EGF)^(1/10)+2.651*(TNFRSF1A)^(1/10)+2.641*(LEP)^(1/10)+4.026*(VEGFA)^(1/10)−1.47*(VCAM1)^(1/10);

PSJC=−25.444+4.051*(SAA1)^(1/10)+16.154*(IL6)^(1/10)−11.847*(EGF)^(1/10)+3.091*(CHI3L1)^(1/10)+0.353*(TNFRSF1A)^(1/10);

PPGHA=−13.489+5.474*(IL6)^(1/10)+0.486*(SAA1)^(1/10)+2.246*(MMP1)^(1/10)+1.684*(leptin)^(1/10)+4.14*(TNFRSF1A)^(1/10)+2.292*(VEGFA)^(1/10)−1.898*(EGF)^(1/10)+0.028*(MMP3)^(1/10)−2.892*(VCAM1)^(1/10)−0.506*(RETN)^(1/10);

IPTJC=max(0.1739*PTJC+0.7865*PSJC,0);

IPSJC=max(0.1734*PTJC+0.7839*PSJC,0);

DAIscore=round(max(min((0.56*sqrt(IPTJC)+0.28*sqrt(IPSJC)+0.14*PPGA+0.36*ln(CRP/10⁶+1))*10.53+1,100),1)).

For the final DA algorithm, the results from the 11-marker+CRP CW-Lassomodel were scaled and rounded to be integers on a scale of 1-100 suchthat a DAI score of 1 would be equivalent to a DAS28-CRP value of 0, anda DAI score of 100 would be equivalent to a DAS28-CRP value of 9.4.

Gene names in the above formulas correspond to serum proteinconcentrations, as obtained by the MSD® platform. Biomarkerconcentrations were obtained in the ranges shown in Table 20 (95%interval).

TABLE 20 pg/ml Biomarker Lower Limit Upper Limit IL6 2.2 104 EGF 20 383VEGFA 83 776 LEP 2,226 139,885 SAA1 636,889 99,758,140 VCAM1 354,0261,054,681 CRP 245,332 76,399,801 MMP1 3,047 39,373 MMP3 9,203 134,262TNFRSF1A 1,139 4,532 RETN 3,635 19,308 CHI3L1 25,874 442,177

DAI Algorithm Performance

In order to independently test the performance of the algorithmdeveloped above in this Example, a total of 120 serum samples wereanalyzed, obtained from the CAMERA study (see Example 7 for adescription of the CAMERA study). Of these, 72 samples were taken fromsubject baseline visits, and 48 were from visits six months subsequentto baseline. The concentrations of 23 serum protein biomarker weremeasured in each sample: APOA1, APOC3, calprotectin, CCL22, CHI3L1(YKL40), CRP, EGF, ICAM1, IL18, IL1B, IL1RA, IL6, IL6R, IL8, LEP, MMP1,MMP3, PYD, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA. The concentrations ofthe markers were determined by customized immunoassays using either theMeso Scale Discovery SECTOR Imager 6000 or individual ELISAs.

The associations between individual biomarkers and the clinicalassessment measurements of DAS28-CRP, SJC28 and TJC28 were assessed byPearson correlation (r) for log-transformed concentrations. Thecorrelation p-values were adjusted for multiple hypothesis testing byestimating false discovery rates (FDR) using the method of Benjamini andHochberg. See J. Royal Stat. Soc. B 1995 57(1):289-300.

Of the 23 proteins examined, fourteen were statistically significantlycorrelated with DAS28-CRP, eleven with SJC28 and nine with TJC28(FDR<0.05). See Table 22, which shows the Pearson correlations (r)between individual biomarkers and each clinical disease activitymeasure. The q-values reflect the FDRs, and were calculated by adjustingthe p-values for multiple hypothesis testing. Statistically significantassociations (q<0.05) are in bold. As Table 21 shows, the individualbiomarkers associated with disease activity represented a range ofpathways associated with RA disease pathophysiology (FunctionalCategory).

TABLE 21 DAS28-CRP SJC28 TJC28 Biomarker Functional Category r q-val rq-val r q-val calprotectin cytokines and receptors 0.56 <0.01 0.38 <0.010.33 <0.01 CHI3L1 Skeletal 0.42 <0.01 0.35 <0.01 0.30 <0.01 CCL22cytokines and receptors −0.04 0.75 −0.13 0.19 −0.03 0.73 CRP acute phaseresponse 0.69 <0.01 0.41 <0.01 0.36 <0.01 EGF growth factors −0.07 0.46−0.08 0.42 −0.12 0.28 ICAM1 adhesion molecules 0.23 0.02 0.13 0.20 0.080.44 IL1B cytokines and receptors 0.45 <0.01 0.34 <0.01 0.31 <0.01 IL6cytokines and receptors 0.69 <0.01 0.50 <0.01 0.41 <0.01 IL6R cytokinesand receptors 0.01 0.97 0.03 0.71 0.02 0.89 IL8 cytokines and receptors0.47 <0.01 0.46 <0.01 0.30 <0.01 IL1RA cytokines and receptors 0.01 0.970.05 0.58 −0.09 0.44 LEP hormones 0.00 0.97 −0.07 0.53 −0.06 0.56 MMP1MMPs 0.36 <0.01 0.29 <0.01 0.19 0.06 MMP3 MMPs 0.51 <0.01 0.40 <0.010.26 <0.01 PYD skeletal 0.23 0.04 0.29 <0.01 0.21 0.09 RETN hormones0.22 0.03 0.13 0.20 0.13 0.28 SAA1 acute phase response 0.66 <0.01 0.43<0.01 0.37 <0.01 TNFRSF1A cytokines and receptors 0.36 <0.01 0.30 <0.010.24 0.02 VCAM1 adhesion molecules 0.13 0.24 0.14 0.20 0.08 0.56 VEGFAgrowth factors 0.29 <0.01 0.18 0.12 0.07 0.56

Two pre-specified algorithms, a prototype and a final algorithm, usingsubsets of these 23 biomarkers were applied to calculate a total DAIscore for each subject at each visit (baseline and six-month). Thesealgorithms were trained in prior studies using independent samples fromother clinical cohorts. Algorithm performance was evaluated by Pearsoncorrelation (r) and area under the ROC curve (AUROC) for identifyinghigh and low disease activity at the baseline and six-month visits. Thereference classification for ROC analysis was based on a DAS28-CRP of2.67, the threshold separating remission/low disease activity frommoderate and high disease activity.

Prototype Algorithm for Multivariate Model

The first algorithm, or “prototype algorithm,” using a linearcombination of protein biomarkers, was trained on subject samples toestimate the DAS28 directly and was provided by the formula describedelsewhere herein according to:

DAI=b ₀ +b ₁*DAIMRK₁ ^(x) −b ₂*DAIMRK₂ ^(x) −b ₃*DAIMRK₃ ^(x) . . . −b_(n)*DAIMRK_(n) ^(x);

where DAI is the DAI score, b_(0-n) are constants, and DAIMRK_(1-n) ^(x)are the serum concentrations, transformed to the x^(th) power, of ndifferent biomarkers selected from the DAIMRK panel.

The prototype algorithm used in this Example was:

DAI=(−16.1564)−(0.0606*Calprotectin^(1/10))+(0.2194*CH3L1^(1/10))+(1.1886*ICAM1^(1/10))+(2.7738*IL6^(1/10))+(0.7254*MMP1^(1/10))−(0.8348*MMP3^(1/10))+(1.0296*PYD^(1/10))+(1.1792*SAA1^(1/10))+(2.4422*TNFRSF1A^(1/10))+(0.3272*VEGFA^(1/10)).

The prototype algorithm achieved a Pearson correlation (r) of 0.65 andan AUROC of 0.84 relative to the DAS28-CRP.

Biomarker Selection for Final Algorithm

The second algorithm was derived using serum biomarker concentrations toseparately estimate the three clinical assessments of TJC28, SJC28 andPGHA. Note that all of these are components of the formula used incalculating DAS28-CRP:

DAS28-CRP=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1)+(0.014*PGHA)+0.96.

Biomarkers were then selected to predict and estimate clinicalassessments of disease activity, specifically PGHA, TJC28 and SJC28. Theresulting estimates were combined with a serum CRP concentrationmeasurement to calculate an overall DAI score. See FIG. 22, whichindicates the three panels of biomarkers predictive of clinical diseaseactivity measurements, the union thereof, and CRP. The CW-Lasso methodwas used to predict the individual components of the DAS28; i.e., TJC28,SJC28 and PGHA. Note that biomarker terms are included in the CW-Lassoif they help to improve cross-validated model performance, and thiscriterion does not imply that each term is statistically significant byunivariate analysis. A biomarker could make a significant contributionto a multivariate model even if it does not have a significantunivariate correlation, and could not make a significant contribution toa multivariate model even though it has a significant univariatecorrelation. Indeed, a comparison of each algorithm predictive for aclinical assessment, (a)-(c) above, with the biomarkers of Table 18shows that not all biomarkers in each algorithm were individuallystatistically correlated with that clinical assessment. For example,values for serum concentrations of EGF, LEP, VEGFA and VCAM1 are allincluded in the algorithm for predicting TJC28, yet each of thesemarkers individually demonstrated a q-value for correlation with TJC of≧0.28. Including these markers, however, improves multivariate modelperformance in independent cross-validation test sets.

The overall DAI score derived according to the methods of the presentExample was given as a whole number between 1 and 100. The formula usedto derive this score was provided by:

DAIScore=((0.56*sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/10⁶+1)+(0.14*PPGHA)+0.96)*10.53)+1,

where PTJC=predicted TJC28, PSJC=predicted SJC28, and PPGHA=predictedPGA. Unlike other formulas to derive DAI scores described herein, in theformula of this Example the measurements of individual biomarkers wereweighted based on information such as that depicted in FIG. 22, andremoving redundancy of biomarkers, so as to derive the best predictionof and correlation with particular clinical disease activitymeasurements (TJC28, SJC28, PGHA). This resulted in the inclusion ofdata from the following set of biomarkers: SAA1, IL6, CHI3L1, EGF,TNFRSF1A, LEP, VEGFA and VCAM1 for PTJC; SAA1, IL6, EGF, CHI3L1 andTNFRSF1A for PSJC; SAA1, MMP1, LEP, TNFRSF1A, VEGFA, EGF, MMP3, VCAM1and RETN for PPGHA; plus CRP. In total, therefore, data from thefollowing set of 12 markers was used to derive a DAI score: CHI3L1, CRP,EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1 and VEGFA. Thepredicted clinical assessments of disease activity were developedaccording to the following formulas:

PTJC=−38.564+(3.997*SAA1^(1/10))+(17.331*IL6^(1/10))+(4.665*CHI3L1^(1/10))−(15.236*EGF^(1/10))+(2.651*TNFRSF1A^(1/10))+(2.641*LEP^(1/10))+(4.026*VEGFA^(1/10))−(1.47*VCAM1^(1/10))  (a)

PSJC=−25.444+(4.051*SAA1^(1/10))+(16.154*IL6^(1/10))−(11.847*EGF^(1/10))+(3.091*CHI3L1^(1/10))+(0.353*TNFRSF1A^(1/10));and,  (b)

PPGHA=−13.489+(5.474*IL6^(1/10))+(0.486*SAA1^(1/10))+(2.246*MMP1^(1/10))+(1.684*LEP^(1/10))+(4.14*TNFRSF1A^(1/10))+(2.292*VEGFA^(1/10))−(1.898*EGF^(1/10))+(0.028*MMP3^(1/10))−(2.892*VCAM1^(1/10))−(0.506*RETN^(1/10)).  (c)

The performance of the above algorithm in deriving a DAI score wasevaluated by Pearson correlation (r) and area under the ROC curve(AUROC) for identifying high and low disease activity at the baselineand six-month visits. The Pearson correlation was 0.73, and the AUROCwas 0.87, with the reference classification for ROC analysis based on athreshold DAS28-CRP of 2.67, the threshold separating remission/lowdisease activity from moderate and high disease activity. The changes inbiomarker-based DAI scores between the baseline and six-month visitswere assessed by the paired Wilcoxon rank sum test.

To ensure that performance of the second algorithm was not overestimateddue to the inclusion of two samples for some patients, subsets ofsamples were also analyzed that included only one randomly selectedvisit for each subject. The algorithm performed equally well in thesesubsets. Possible bias in the AUROC due to an imbalance in numbersbetween low and high disease activity groups was also analyzed using aDAS28-CRP cutoff of 2.67. When the cutoff was set at the medianDAS28-CRP of 4.6, the AUROC was 0.83.

When the predictions of the individual components of the DAS28 generatedby the DAI algorithm were correlated to the actual TJC28, SJC28 andPGHA, the correlation coefficients were seen to trend higher (and thusprovide better correlation with clinical disease activity measurements)than the coefficients for CRP, a marker commonly used alone as anindicator of RA disease activity. See FIG. 23.

An analysis was then done to determine whether the DAI score changed inresponse to the treatment protocols used in the CAMERA study. For allsubjects for whom DAI Scores were available for both visits (baselineand six-month), the median score dropped from 52 to 37 (p=2.2E-6; n=46).See FIG. 24. The intensive and conventional treatment arms wereconsidered separately. There was also a significant decrease in medianDAI Score in the intensive treatment arm, from 52 to 36 (p=2.5E-5;n=31). In the conventional treatment arm, the median DAI Score decreasedfrom 59 to 45 (p=0.06; n=15).

In conclusion, this Example demonstrates that serum protein biomarkersrepresenting a variety of biological pathways were consistentlyassociated with RA disease activity. A pre-specified DAI algorithmcombining information from several of these biomarkers performed well inpredicting RA disease activity when evaluated in an independent testset. The algorithm's estimates of TJC, SJC and PGHA correlated to actualclinical measures of disease activity. Furthermore, subsequent DAIscores of the subjects analyzed decreased compared to initial DAI scoresfollowing and in response to treatment.

Example 12 Use of DAI to Predict Lint Damage Progression

Example 12 demonstrates the use of the DAI score to predict joint damageprogression in RA subjects. In this Example serum samples were analyzedfrom 89 subject participants in the BeSt (Dutch, “Behandelstrategieen”)study. The BeSt study is a multicenter, randomized, controlled studydesigned to compare the clinical efficacy and radiologic outcomes offour different treatment strategies in patients with early onset RA. SeeY P Goekoop-Ruiterman et al., Arth. Rheum. 2005, 52:3381-3390. Serumbiomarkers were evaluated in serum collected at year 1. Total Van derHeijde modified Sharp scores (TSS) from year 1 and year 2 were used.

The DAI score at year 1 was evaluated for its ability to predict thechange in TSS from year 1 to year 2. Identifying patients at risk ofincrease in TSS is a clinical question of great importance. The DAIscore was correlated with change in TSS (P<0.001). See Table 22.Moreover, the observed correlation coefficient for DAI score was greaterthan for any clinical variable examined except year 1 TSS. Since TSS isonly evaluated in clinical trials and not available in routine clinicalpractice, this suggests that the DAI score has the potential tooutperform conventional clinical variables at predicting progressivejoint damage. DAI score also had a higher observed area under thereceiver operating characteristic curve for identifying patients withincreases in TSS than other clinical variables except year 1 TSS.

TABLE 22 P value Correlation AUROC TSS <0.001 0.541 0.765 DAI <0.0010.435 0.686 CRP <0.001 0.366 0.64 ESR 0.027 0.216 0.527 DAS-ESR 0.0010.33 0.567 DAS-CRP 0.001 0.351 0.595 TJC28 0.012 0.252 0.492 SJC28 0.0030.3 0.653 RAI 0.164 0.11 0.485 SJC44 0.106 0.14 0.56 VAS 0.06 0.1740.554

Example 13 DAI Score Unaffected by Comorbidities

512 subjects were selected from the InFoRM cohort, to be representativeof the entire cohort in age, sex, DAS28CRP (DAS28) and disease duration.The ratios in the median CRP, CDAI, DAS28 and DAI in patients withco-morbidities were compared to patients without the co-morbidity toassess the robustness of the DAI. To calculate the DAI, theconcentrations of IL-6, EGF, VEGF-A, Leptin, SAA, CRP, VCAM-1, MMP-1,MMP-3, Resistin, YKL-40, and TNF-RI were measured using multipleximmunoassays and combined in the algorithm identified in Example 11.Co-morbidities of interest included hypertension, osteoarthritis, priorfracture, diabetes, psychiatric illness, peptic ulcer, Sjogren'ssyndrome, fibromyalgia, COPD, and asthma. The significance ofdifferences was determined by Wilcoxon rank sum test with a multipletesting correction applied. The multiple testing correction is describedin Benjamini and Hochberg. J. Royal Stat. Soc. B 1995 57(1):289-300.Results are reported as the ratio of the median value of the measure(e.g. CDAI) among people with the condition compared to those withoutthe condition.

The results showed that several co-morbidities were associated withdifferences, mostly increases, in median disease activity measures.Comparing people with each comorbidity to those without the comorbidity,the ratios in the median scores were generally larger for CRP [range0.8-2.1] and CDAI [range 1.0-1.8] than for DAS28 [range 1.0-1.4] and DAI[range 1.0-1.2]. Across the 4 outcome measures, the greatest number ofsignificant differences in median scores was seen in patients withfibromyalgia, psychiatric illness, Sjogren's, and hypertension (Table1). The DAI was not significantly different in males versus females(median: 41.7 vs. 423, p-value: 0.46) or in current smokers versusnon-smokers (median: 38.5 vs. 42.7, p-value: 0.13). The score did varysignificantly with BMI: median DAI score for subjects with BMI≦30 was38.7, while the median for subjects with a BMI>30 was 46.3.

TABLE 23 Ratios in Disease Activity Measure's Median Value N Subgroup(%) CRP CDAI DAS28 DAI Fibromyalgia 33 1.6* 1.6* 1.3* 1.1 (6)Psychiatric 24 1.7 1.7* 1.4* 1.1 illness (5) Sjogren's 20 1.0 1.8* 1.3*1.1 (4) Hypertension 223 1.0 1.3* 1.1* 1.1  (44) Peptic Ulcer 19 0.81.5* 1.2 1.0 (4) Osteoarthritis 173 1.0 1.2 1.1 1.0  (34) Osteoporotic131 0.9 1.0 1.0 1.0 bone fracture  (26) Diabetes 72 0.9 1.1 1.1 1.1 (14) Asthma 50 1.5 1.2 1.1 1.1  (10) COPD 20 2.1 1.1 10 1.2 (4) A valueof 1.0 implies that there is no difference in the median value of themeasure for people with versus those without the comorbidity*Significant difference from the population without the co-morbidity,False Discovery Rate <10%.

In conclusion, DAI has been validated to assess and monitor rheumatoidarthritis (“RA”) disease activity. When assessing the RA diseaseactivity of patients with common co-morbidities, the DAI appears to beless confounded by the presence of co-morbidities than the othermeasures tested. This may be due to its inclusion of multiple biomarkersrepresenting biologic pathways in RA.

Example 14 DAI Score to Measure Disease Activity in UndifferentiatedArthritis

It has been shown that DAS is a valid measure of disease activity inundifferentiated arthritis (“UA”). See Fransen, J. et al. Arthritis Careand Research, 62(10):1392.8, 2010. Thus, the model in example 11, whichestimates the DAS, calculates a DAI score which is a measure of UAdisease activity. Alternatively a model similar to that in example 11 istrained such that the DAI score is a measure of UA disease activity.

All publications and patent applications cited in this specification areherein incorporated by reference as if each individual publication orpatent application were specifically and individually indicated to beincorporated by reference.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it will be readily apparent to one of ordinary skill inthe art in light of the teachings of this invention that certain changesand modifications may be made thereto without departing from the spiritor scope of the invention as defined in the appended claims.

1.-231. (canceled)
 232. A method for generating quantitative data for afirst subject comprising: performing at least one immunoassay on a firstsample from the first subject to generate a first dataset comprising thequantitative data, wherein the quantitative data represents at leasttwelve protein markers comprising: chitinase 3-like 1 (cartilageglycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP);epidermal growth factor (beta-urogastrone) (EGF); interleukin 6(interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1(interstitial collagenase) (MMP1); matrix metallopeptidase 3(stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1(SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and, vascularendothelial growth factor A (VEGFA); and wherein the first subject hasrheumatoid arthritis (RA) or is suspected of having RA.
 233. The methodof claim 232, wherein the at least twelve protein markers consist of:IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, andCHI3L1.
 234. The method of claim 232, wherein performance of the atleast one immunoassay comprises: obtaining the first sample, wherein thefirst sample comprises the protein markers; contacting the first samplewith a plurality of distinct reagents; generating a plurality ofdistinct complexes between the reagents and markers; and detecting thecomplexes to generate the data.
 235. The method of claim 232, whereinthe at least one immunoassay comprises a multiplex assay.